diff --git a/.env b/.env
index d7a48b8..dd402ce 100644
--- a/.env
+++ b/.env
@@ -1,10 +1,12 @@
 PROJECT_NAME="Wordcab Transcribe"
 VERSION="0.1.0"
-DESCRIPTION="ASR FastAPI server using faster-whisper and pyannote-audio."
+DESCRIPTION="💬 ASR FastAPI server using faster-whisper and NVIDIA NeMo."
 API_PREFIX="/api/v1"
 DEBUG=True
 BATCH_SIZE=1
 MAX_WAIT=0.1
 WHISPER_MODEL="large-v2"
-EMBEDDINGS_MODEL="speechbrain/spkrec-ecapa-voxceleb"
 COMPUTE_TYPE="int8_float16"
+NEMO_DOMAIN_TYPE="telephonic"  # Can be general, meeting or telephonic based on domain type of the audio file
+NEMO_STORAGE_PATH="nemo_storage"
+NEMO_OUTPUT_PATH="nemo_outputs"
diff --git a/Dockerfile b/Dockerfile
index 8e86eeb..6041197 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,19 +1,17 @@
-FROM nvidia/cuda:11.7.0-devel-ubuntu22.04
+FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu20.04
 
+ENV DEBIAN_FRONTEND=noninteractive
 RUN apt-get update && apt-get install -y \
     git \
     curl \
     ffmpeg \
     libsndfile1 \
-    software-properties-common
-RUN add-apt-repository ppa:deadsnakes/ppa \
-    && apt install -y python3.10 \
-    && rm -rf /var/lib/apt/lists/* \
-    && curl -sS https://bootstrap.pypa.io/get-pip.py | python3.10
-    
+    software-properties-common \
+    python3-pip
+
 COPY requirements.txt /requirements.txt
-RUN python3.10 -m pip install -r requirements.txt
-RUN python3.10 -m pip install --upgrade torch==1.13.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
+RUN python3 -m pip install -r requirements.txt
+RUN python3 -m pip install --upgrade torch==2.0.0+cu118 torchaudio==2.0.1 --extra-index-url https://download.pytorch.org/whl/cu118
 
 COPY . /app
 WORKDIR /app
diff --git a/README.md b/README.md
index 8cceb0f..9438e70 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # Wordcab Transcribe 💬
 
-FastAPI based API for transcribing audio files using [`faster-whisper`](https://github.com/guillaumekln/faster-whisper) and [`pyannote-audio`](https://github.com/pyannote/pyannote-audio)
+FastAPI based API for transcribing audio files using [`faster-whisper`](https://github.com/guillaumekln/faster-whisper) and [`NVIDIA NeMo`](https://github.com/NVIDIA/NeMo)
 
 More details on this project on this [blog post](https://wordcab.github.io/wordcab-posts/blog/2023/03/31/wordcab-transcribe/).
 
@@ -54,9 +54,8 @@ import requests
 
 headers = {"accept": "application/json"}
 data = {
-  "num_speakers": 1,  # optional, default is 0
   "source_lang": "en",  # optional, default is "en"
-  "timestamps": "seconds",  # optional, default is "seconds"
+  "timestamps": "s",  # optional, default is "s". Can be "s", "ms" or "hms".
 }
 
 filepath = "tests/sample_1.mp3"  # or any other audio file. Prefer wav files.
@@ -80,9 +79,8 @@ import requests
 headers = {"accept": "application/json", "Content-Type": "application/json"}
 params = {"url": "https://youtu.be/JZ696sbfPHs"}
 data = {
-  "num_speakers": 1,  # optional, default is 0
   "source_lang": "en",  # optional, default is "en"
-  "timestamps": "seconds",  # optional, default is "seconds"
+  "timestamps": "s",  # optional, default is "s". Can be "s", "ms" or "hms".
 }
 
 response = requests.post(
diff --git a/config/nemo/diar_infer_general.yaml b/config/nemo/diar_infer_general.yaml
new file mode 100644
index 0000000..941622b
--- /dev/null
+++ b/config/nemo/diar_infer_general.yaml
@@ -0,0 +1,91 @@
+# This YAML file is created for all types of offline speaker diarization inference tasks in `<NeMo git root>/example/speaker_tasks/diarization` folder.
+# The inference parameters for VAD, speaker embedding extractor, clustering module, MSDD module, ASR decoder are all included in this YAML file.
+# All the keys under `diarizer` key (`vad`, `speaker_embeddings`, `clustering`, `msdd_model`, `asr`) can be selectively used for its own purpose and also can be ignored if the module is not used.
+# The configurations in this YAML file is optimized to show balanced performances on various types of domain. VAD is optimized on multilingual ASR datasets and diarizer is optimized on DIHARD3 development set.
+# An example line in an input manifest file (`.json` format):
+# {"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, "label": "infer", "text": "-", "num_speakers": null, "rttm_filepath": "/path/to/rttm/file", "uem_filepath": "/path/to/uem/file"}
+name: &name "ClusterDiarizer"
+
+num_workers: 1
+sample_rate: 16000
+batch_size: 64
+device: null # can specify a specific device, i.e: cuda:1 (default cuda if cuda available, else cpu)
+verbose: True # enable additional logging
+
+diarizer:
+  manifest_filepath: ???
+  out_dir: ???
+  oracle_vad: False # If True, uses RTTM files provided in the manifest file to get speech activity (VAD) timestamps
+  collar: 0.25 # Collar value for scoring
+  ignore_overlap: True # Consider or ignore overlap segments while scoring
+
+  vad:
+    model_path: vad_multilingual_marblenet # .nemo local model path or pretrained VAD model name
+    external_vad_manifest: null # This option is provided to use external vad and provide its speech activity labels for speaker embeddings extraction. Only one of model_path or external_vad_manifest should be set
+
+    parameters: # Tuned by detection error rate (false alarm + miss) on multilingual ASR evaluation datasets
+      window_length_in_sec: 0.63 # Window length in sec for VAD context input
+      shift_length_in_sec: 0.08 # Shift length in sec for generate frame level VAD prediction
+      smoothing: False # False or type of smoothing method (eg: median)
+      overlap: 0.5 # Overlap ratio for overlapped mean/median smoothing filter
+      onset: 0.5 # Onset threshold for detecting the beginning and end of a speech
+      offset: 0.3 # Offset threshold for detecting the end of a speech
+      pad_onset: 0.2 # Adding durations before each speech segment
+      pad_offset: 0.2 # Adding durations after each speech segment
+      min_duration_on: 0.5 # Threshold for small non_speech deletion
+      min_duration_off: 0.5 # Threshold for short speech segment deletion
+      filter_speech_first: True
+
+  speaker_embeddings:
+    model_path: titanet_large # .nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet)
+    parameters:
+      window_length_in_sec: [1.9, 1.2, 0.5] # Window length(s) in sec (floating-point number). either a number or a list. ex) 1.5 or [1.5,1.0,0.5]
+      shift_length_in_sec: [0.95, 0.6, 0.25] # Shift length(s) in sec (floating-point number). either a number or a list. ex) 0.75 or [0.75,0.5,0.25]
+      multiscale_weights: [1, 1, 1] # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. ex) [0.33,0.33,0.33]
+      save_embeddings: True # If True, save speaker embeddings in pickle format. This should be True if clustering result is used for other models, such as `msdd_model`.
+
+  clustering:
+    parameters:
+      oracle_num_speakers: False # If True, use num of speakers value provided in manifest file.
+      max_num_speakers: 8 # Max number of speakers for each recording. If an oracle number of speakers is passed, this value is ignored.
+      enhanced_count_thres: 80 # If the number of segments is lower than this number, enhanced speaker counting is activated.
+      max_rp_threshold: 0.25 # Determines the range of p-value search: 0 < p <= max_rp_threshold.
+      sparse_search_volume: 10 # The higher the number, the more values will be examined with more time.
+      maj_vote_spk_count: False # If True, take a majority vote on multiple p-values to estimate the number of speakers.
+
+  msdd_model:
+    model_path: null # .nemo local model path or pretrained model name for multiscale diarization decoder (MSDD)
+    parameters:
+      use_speaker_model_from_ckpt: True # If True, use speaker embedding model in checkpoint. If False, the provided speaker embedding model in config will be used.
+      infer_batch_size: 25 # Batch size for MSDD inference.
+      sigmoid_threshold: [0.7] # Sigmoid threshold for generating binarized speaker labels. The smaller the more generous on detecting overlaps.
+      seq_eval_mode: False # If True, use oracle number of speaker and evaluate F1 score for the given speaker sequences. Default is False.
+      split_infer: True # If True, break the input audio clip to short sequences and calculate cluster average embeddings for inference.
+      diar_window_length: 50 # The length of split short sequence when split_infer is True.
+      overlap_infer_spk_limit: 5 # If the estimated number of speakers are larger than this number, overlap speech is not estimated.
+
+  asr:
+    model_path: null # Provide NGC cloud ASR model name. stt_en_conformer_ctc_* models are recommended for diarization purposes.
+    parameters:
+      asr_based_vad: False # if True, speech segmentation for diarization is based on word-timestamps from ASR inference.
+      asr_based_vad_threshold: 1.0 # Threshold (in sec) that caps the gap between two words when generating VAD timestamps using ASR based VAD.
+      asr_batch_size: null # Batch size can be dependent on each ASR model. Default batch sizes are applied if set to null.
+      decoder_delay_in_sec: null # Native decoder delay. null is recommended to use the default values for each ASR model.
+      word_ts_anchor_offset: null # Offset to set a reference point from the start of the word. Recommended range of values is [-0.05  0.2].
+      word_ts_anchor_pos: "start" # Select which part of the word timestamp we want to use. The options are: 'start', 'end', 'mid'.
+      fix_word_ts_with_VAD: False # Fix the word timestamp using VAD output. You must provide a VAD model to use this feature.
+      colored_text: False # If True, use colored text to distinguish speakers in the output transcript.
+      print_time: True # If True, the start and end time of each speaker turn is printed in the output transcript.
+      break_lines: False # If True, the output transcript breaks the line to fix the line width (default is 90 chars)
+
+    ctc_decoder_parameters: # Optional beam search decoder (pyctcdecode)
+      pretrained_language_model: null # KenLM model file: .arpa model file or .bin binary file.
+      beam_width: 32
+      alpha: 0.5
+      beta: 2.5
+
+    realigning_lm_parameters: # Experimental feature
+      arpa_language_model: null # Provide a KenLM language model in .arpa format.
+      min_number_of_words: 3 # Min number of words for the left context.
+      max_number_of_words: 10 # Max number of words for the right context.
+      logprob_diff_threshold: 1.2 # The threshold for the difference between two log probability values from two hypotheses.
diff --git a/config/nemo/diar_infer_meeting.yaml b/config/nemo/diar_infer_meeting.yaml
new file mode 100644
index 0000000..e03cd18
--- /dev/null
+++ b/config/nemo/diar_infer_meeting.yaml
@@ -0,0 +1,91 @@
+# This YAML file is created for all types of offline speaker diarization inference tasks in `<NeMo git root>/example/speaker_tasks/diarization` folder.
+# The inference parameters for VAD, speaker embedding extractor, clustering module, MSDD module, ASR decoder are all included in this YAML file.
+# All the keys under `diarizer` key (`vad`, `speaker_embeddings`, `clustering`, `msdd_model`, `asr`) can be selectively used for its own purpose and also can be ignored if the module is not used.
+# The configurations in this YAML file is suitable for 3~5 speakers participating in a meeting and may not show the best performance on other types of dialogues.
+# An example line in an input manifest file (`.json` format):
+# {"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, "label": "infer", "text": "-", "num_speakers": null, "rttm_filepath": "/path/to/rttm/file", "uem_filepath": "/path/to/uem/file"}
+name: &name "ClusterDiarizer"
+
+num_workers: 1
+sample_rate: 16000
+batch_size: 64
+device: null # can specify a specific device, i.e: cuda:1 (default cuda if cuda available, else cpu)
+verbose: True # enable additional logging
+
+diarizer:
+  manifest_filepath: ???
+  out_dir: ???
+  oracle_vad: False # If True, uses RTTM files provided in the manifest file to get speech activity (VAD) timestamps
+  collar: 0.25 # Collar value for scoring
+  ignore_overlap: True # Consider or ignore overlap segments while scoring
+
+  vad:
+    model_path: vad_multilingual_marblenet # .nemo local model path or pretrained VAD model name
+    external_vad_manifest: null # This option is provided to use external vad and provide its speech activity labels for speaker embeddings extraction. Only one of model_path or external_vad_manifest should be set
+
+    parameters: # Tuned parameters for CH109 (using the 11 multi-speaker sessions as dev set)
+      window_length_in_sec: 0.63 # Window length in sec for VAD context input
+      shift_length_in_sec: 0.01 # Shift length in sec for generate frame level VAD prediction
+      smoothing: False # False or type of smoothing method (eg: median)
+      overlap: 0.5 # Overlap ratio for overlapped mean/median smoothing filter
+      onset: 0.9 # Onset threshold for detecting the beginning and end of a speech
+      offset: 0.5 # Offset threshold for detecting the end of a speech
+      pad_onset: 0 # Adding durations before each speech segment
+      pad_offset: 0 # Adding durations after each speech segment
+      min_duration_on: 0 # Threshold for small non_speech deletion
+      min_duration_off: 0.6 # Threshold for short speech segment deletion
+      filter_speech_first: True
+
+  speaker_embeddings:
+    model_path: titanet_large # .nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet)
+    parameters:
+      window_length_in_sec: [3.0, 2.5, 2.0, 1.5, 1.0, 0.5] # Window length(s) in sec (floating-point number). either a number or a list. ex) 1.5 or [1.5,1.0,0.5]
+      shift_length_in_sec: [1.5, 1.25, 1.0, 0.75, 0.5, 0.25] # Shift length(s) in sec (floating-point number). either a number or a list. ex) 0.75 or [0.75,0.5,0.25]
+      multiscale_weights: [1, 1, 1, 1, 1, 1] # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. ex) [0.33,0.33,0.33]
+      save_embeddings: True # If True, save speaker embeddings in pickle format. This should be True if clustering result is used for other models, such as `msdd_model`.
+
+  clustering:
+    parameters:
+      oracle_num_speakers: False # If True, use num of speakers value provided in manifest file.
+      max_num_speakers: 8 # Max number of speakers for each recording. If an oracle number of speakers is passed, this value is ignored.
+      enhanced_count_thres: 80 # If the number of segments is lower than this number, enhanced speaker counting is activated.
+      max_rp_threshold: 0.25 # Determines the range of p-value search: 0 < p <= max_rp_threshold.
+      sparse_search_volume: 30 # The higher the number, the more values will be examined with more time.
+      maj_vote_spk_count: False # If True, take a majority vote on multiple p-values to estimate the number of speakers.
+
+  msdd_model:
+    model_path: null # .nemo local model path or pretrained model name for multiscale diarization decoder (MSDD)
+    parameters:
+      use_speaker_model_from_ckpt: True # If True, use speaker embedding model in checkpoint. If False, the provided speaker embedding model in config will be used.
+      infer_batch_size: 25 # Batch size for MSDD inference.
+      sigmoid_threshold: [0.7] # Sigmoid threshold for generating binarized speaker labels. The smaller the more generous on detecting overlaps.
+      seq_eval_mode: False # If True, use oracle number of speaker and evaluate F1 score for the given speaker sequences. Default is False.
+      split_infer: True # If True, break the input audio clip to short sequences and calculate cluster average embeddings for inference.
+      diar_window_length: 50 # The length of split short sequence when split_infer is True.
+      overlap_infer_spk_limit: 5 # If the estimated number of speakers are larger than this number, overlap speech is not estimated.
+
+  asr:
+    model_path: stt_en_conformer_ctc_large # Provide NGC cloud ASR model name. stt_en_conformer_ctc_* models are recommended for diarization purposes.
+    parameters:
+      asr_based_vad: False # if True, speech segmentation for diarization is based on word-timestamps from ASR inference.
+      asr_based_vad_threshold: 1.0 # Threshold (in sec) that caps the gap between two words when generating VAD timestamps using ASR based VAD.
+      asr_batch_size: null # Batch size can be dependent on each ASR model. Default batch sizes are applied if set to null.
+      decoder_delay_in_sec: null # Native decoder delay. null is recommended to use the default values for each ASR model.
+      word_ts_anchor_offset: null # Offset to set a reference point from the start of the word. Recommended range of values is [-0.05  0.2].
+      word_ts_anchor_pos: "start" # Select which part of the word timestamp we want to use. The options are: 'start', 'end', 'mid'.
+      fix_word_ts_with_VAD: False # Fix the word timestamp using VAD output. You must provide a VAD model to use this feature.
+      colored_text: False # If True, use colored text to distinguish speakers in the output transcript.
+      print_time: True # If True, the start and end time of each speaker turn is printed in the output transcript.
+      break_lines: False # If True, the output transcript breaks the line to fix the line width (default is 90 chars)
+
+    ctc_decoder_parameters: # Optional beam search decoder (pyctcdecode)
+      pretrained_language_model: null # KenLM model file: .arpa model file or .bin binary file.
+      beam_width: 32
+      alpha: 0.5
+      beta: 2.5
+
+    realigning_lm_parameters: # Experimental feature
+      arpa_language_model: null # Provide a KenLM language model in .arpa format.
+      min_number_of_words: 3 # Min number of words for the left context.
+      max_number_of_words: 10 # Max number of words for the right context.
+      logprob_diff_threshold: 1.2 # The threshold for the difference between two log probability values from two hypotheses.
diff --git a/config/nemo/diar_infer_telephonic.yaml b/config/nemo/diar_infer_telephonic.yaml
new file mode 100644
index 0000000..3c98806
--- /dev/null
+++ b/config/nemo/diar_infer_telephonic.yaml
@@ -0,0 +1,91 @@
+# This YAML file is created for all types of offline speaker diarization inference tasks in `<NeMo git root>/example/speaker_tasks/diarization` folder.
+# The inference parameters for VAD, speaker embedding extractor, clustering module, MSDD module, ASR decoder are all included in this YAML file.
+# All the keys under `diarizer` key (`vad`, `speaker_embeddings`, `clustering`, `msdd_model`, `asr`) can be selectively used for its own purpose and also can be ignored if the module is not used.
+# The configurations in this YAML file is suitable for telephone recordings involving 2~8 speakers in a session and may not show the best performance on the other types of acoustic conditions or dialogues.
+# An example line in an input manifest file (`.json` format):
+# {"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, "label": "infer", "text": "-", "num_speakers": null, "rttm_filepath": "/path/to/rttm/file", "uem_filepath": "/path/to/uem/file"}
+name: &name "ClusterDiarizer"
+
+num_workers: 1
+sample_rate: 16000
+batch_size: 64
+device: null # can specify a specific device, i.e: cuda:1 (default cuda if cuda available, else cpu)
+verbose: True # enable additional logging
+
+diarizer:
+  manifest_filepath: ???
+  out_dir: ???
+  oracle_vad: False # If True, uses RTTM files provided in the manifest file to get speech activity (VAD) timestamps
+  collar: 0.25 # Collar value for scoring
+  ignore_overlap: True # Consider or ignore overlap segments while scoring
+
+  vad:
+    model_path: vad_multilingual_marblenet # .nemo local model path or pretrained VAD model name
+    external_vad_manifest: null # This option is provided to use external vad and provide its speech activity labels for speaker embeddings extraction. Only one of model_path or external_vad_manifest should be set
+
+    parameters: # Tuned parameters for CH109 (using the 11 multi-speaker sessions as dev set)
+      window_length_in_sec: 0.15 # Window length in sec for VAD context input
+      shift_length_in_sec: 0.01 # Shift length in sec for generate frame level VAD prediction
+      smoothing: "median" # False or type of smoothing method (eg: median)
+      overlap: 0.5 # Overlap ratio for overlapped mean/median smoothing filter
+      onset: 0.1 # Onset threshold for detecting the beginning and end of a speech
+      offset: 0.1 # Offset threshold for detecting the end of a speech
+      pad_onset: 0.1 # Adding durations before each speech segment
+      pad_offset: 0 # Adding durations after each speech segment
+      min_duration_on: 0 # Threshold for small non_speech deletion
+      min_duration_off: 0.2 # Threshold for short speech segment deletion
+      filter_speech_first: True
+
+  speaker_embeddings:
+    model_path: titanet_large # .nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet)
+    parameters:
+      window_length_in_sec: [1.5, 1.25, 1.0, 0.75, 0.5] # Window length(s) in sec (floating-point number). either a number or a list. ex) 1.5 or [1.5,1.0,0.5]
+      shift_length_in_sec: [0.75, 0.625, 0.5, 0.375, 0.25] # Shift length(s) in sec (floating-point number). either a number or a list. ex) 0.75 or [0.75,0.5,0.25]
+      multiscale_weights: [1, 1, 1, 1, 1] # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. ex) [0.33,0.33,0.33]
+      save_embeddings: True # If True, save speaker embeddings in pickle format. This should be True if clustering result is used for other models, such as `msdd_model`.
+
+  clustering:
+    parameters:
+      oracle_num_speakers: False # If True, use num of speakers value provided in manifest file.
+      max_num_speakers: 8 # Max number of speakers for each recording. If an oracle number of speakers is passed, this value is ignored.
+      enhanced_count_thres: 80 # If the number of segments is lower than this number, enhanced speaker counting is activated.
+      max_rp_threshold: 0.25 # Determines the range of p-value search: 0 < p <= max_rp_threshold.
+      sparse_search_volume: 30 # The higher the number, the more values will be examined with more time.
+      maj_vote_spk_count: False # If True, take a majority vote on multiple p-values to estimate the number of speakers.
+
+  msdd_model:
+    model_path: diar_msdd_telephonic # .nemo local model path or pretrained model name for multiscale diarization decoder (MSDD)
+    parameters:
+      use_speaker_model_from_ckpt: True # If True, use speaker embedding model in checkpoint. If False, the provided speaker embedding model in config will be used.
+      infer_batch_size: 25 # Batch size for MSDD inference.
+      sigmoid_threshold: [0.7] # Sigmoid threshold for generating binarized speaker labels. The smaller the more generous on detecting overlaps.
+      seq_eval_mode: False # If True, use oracle number of speaker and evaluate F1 score for the given speaker sequences. Default is False.
+      split_infer: True # If True, break the input audio clip to short sequences and calculate cluster average embeddings for inference.
+      diar_window_length: 50 # The length of split short sequence when split_infer is True.
+      overlap_infer_spk_limit: 5 # If the estimated number of speakers are larger than this number, overlap speech is not estimated.
+
+  asr:
+    model_path: stt_en_conformer_ctc_large # Provide NGC cloud ASR model name. stt_en_conformer_ctc_* models are recommended for diarization purposes.
+    parameters:
+      asr_based_vad: False # if True, speech segmentation for diarization is based on word-timestamps from ASR inference.
+      asr_based_vad_threshold: 1.0 # Threshold (in sec) that caps the gap between two words when generating VAD timestamps using ASR based VAD.
+      asr_batch_size: null # Batch size can be dependent on each ASR model. Default batch sizes are applied if set to null.
+      decoder_delay_in_sec: null # Native decoder delay. null is recommended to use the default values for each ASR model.
+      word_ts_anchor_offset: null # Offset to set a reference point from the start of the word. Recommended range of values is [-0.05  0.2].
+      word_ts_anchor_pos: "start" # Select which part of the word timestamp we want to use. The options are: 'start', 'end', 'mid'.
+      fix_word_ts_with_VAD: False # Fix the word timestamp using VAD output. You must provide a VAD model to use this feature.
+      colored_text: False # If True, use colored text to distinguish speakers in the output transcript.
+      print_time: True # If True, the start and end time of each speaker turn is printed in the output transcript.
+      break_lines: False # If True, the output transcript breaks the line to fix the line width (default is 90 chars)
+
+    ctc_decoder_parameters: # Optional beam search decoder (pyctcdecode)
+      pretrained_language_model: null # KenLM model file: .arpa model file or .bin binary file.
+      beam_width: 32
+      alpha: 0.5
+      beta: 2.5
+
+    realigning_lm_parameters: # Experimental feature
+      arpa_language_model: null # Provide a KenLM language model in .arpa format.
+      min_number_of_words: 3 # Min number of words for the left context.
+      max_number_of_words: 10 # Max number of words for the right context.
+      logprob_diff_threshold: 1.2 # The threshold for the difference between two log probability values from two hypotheses.
diff --git a/poetry.lock b/poetry.lock
index 0dee452..d2cbfe7 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.4.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -148,26 +148,6 @@ files = [
 [package.dependencies]
 frozenlist = ">=1.1.0"
 
-[[package]]
-name = "alembic"
-version = "1.10.3"
-description = "A database migration tool for SQLAlchemy."
-category = "main"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "alembic-1.10.3-py3-none-any.whl", hash = "sha256:b2e0a6cfd3a8ce936a1168320bcbe94aefa3f4463cd773a968a55071beb3cd37"},
-    {file = "alembic-1.10.3.tar.gz", hash = "sha256:32a69b13a613aeb7e8093f242da60eff9daed13c0df02fff279c1b06c32965d2"},
-]
-
-[package.dependencies]
-Mako = "*"
-SQLAlchemy = ">=1.3.0"
-typing-extensions = ">=4"
-
-[package.extras]
-tz = ["python-dateutil"]
-
 [[package]]
 name = "antlr4-python3-runtime"
 version = "4.9.3"
@@ -201,24 +181,46 @@ test = ["contextlib2", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=
 trio = ["trio (>=0.16,<0.22)"]
 
 [[package]]
-name = "asteroid-filterbanks"
-version = "0.4.0"
-description = "Asteroid's filterbanks"
+name = "appdirs"
+version = "1.4.4"
+description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = "*"
+files = [
+    {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"},
+    {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
+]
+
+[[package]]
+name = "appnope"
+version = "0.1.3"
+description = "Disable App Nap on macOS >= 10.9"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
+    {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
+    {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
+]
+
+[[package]]
+name = "asttokens"
+version = "2.2.1"
+description = "Annotate AST trees with source code positions"
+category = "main"
+optional = false
+python-versions = "*"
 files = [
-    {file = "asteroid-filterbanks-0.4.0.tar.gz", hash = "sha256:415f89d1dcf2b13b35f03f7a9370968ac4e6fa6800633c522dac992b283409b9"},
-    {file = "asteroid_filterbanks-0.4.0-py3-none-any.whl", hash = "sha256:4932ac8b6acc6e08fb87cbe8ece84215b5a74eee284fe83acf3540a72a02eaf5"},
+    {file = "asttokens-2.2.1-py2.py3-none-any.whl", hash = "sha256:6b0ac9e93fb0335014d382b8fa9b3afa7df546984258005da0b9e7095b3deb1c"},
+    {file = "asttokens-2.2.1.tar.gz", hash = "sha256:4622110b2a6f30b77e1473affaa97e711bc2f07d3f10848420ff1898edbe94f3"},
 ]
 
 [package.dependencies]
-numpy = "*"
-torch = ">=1.8.0"
-typing-extensions = "*"
+six = "*"
 
 [package.extras]
-all = ["librosa", "scipy"]
+test = ["astroid", "pytest"]
 
 [[package]]
 name = "async-timeout"
@@ -234,22 +236,22 @@ files = [
 
 [[package]]
 name = "attrs"
-version = "22.2.0"
+version = "23.1.0"
 description = "Classes Without Boilerplate"
 category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
-    {file = "attrs-22.2.0-py3-none-any.whl", hash = "sha256:29e95c7f6778868dbd49170f98f8818f78f3dc5e0e37c0b1f474e3561b240836"},
-    {file = "attrs-22.2.0.tar.gz", hash = "sha256:c9227bfc2f01993c03f68db37d1d15c9690188323c067c641f1a35ca58185f99"},
+    {file = "attrs-23.1.0-py3-none-any.whl", hash = "sha256:1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04"},
+    {file = "attrs-23.1.0.tar.gz", hash = "sha256:6279836d581513a26f1bf235f9acd333bc9115683f14f7e8fae46c98fc50e015"},
 ]
 
 [package.extras]
-cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"]
-dev = ["attrs[docs,tests]"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"]
-tests = ["attrs[tests-no-zope]", "zope.interface"]
-tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy (>=0.971,<0.990)", "mypy (>=0.971,<0.990)", "pympler", "pympler", "pytest (>=4.3.0)", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-mypy-plugins", "pytest-xdist[psutil]", "pytest-xdist[psutil]"]
+cov = ["attrs[tests]", "coverage[toml] (>=5.3)"]
+dev = ["attrs[docs,tests]", "pre-commit"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
+tests = ["attrs[tests-no-zope]", "zope-interface"]
+tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 
 [[package]]
 name = "audioread"
@@ -317,15 +319,15 @@ files = [
 ]
 
 [[package]]
-name = "backports-cached-property"
-version = "1.0.2"
-description = "cached_property() - computed once per instance, cached as attribute"
+name = "backcall"
+version = "0.2.0"
+description = "Specifications for callback functions passed in to an API"
 category = "main"
 optional = false
-python-versions = ">=3.6.0"
+python-versions = "*"
 files = [
-    {file = "backports.cached-property-1.0.2.tar.gz", hash = "sha256:9306f9eed6ec55fd156ace6bc1094e2c86fae5fb2bf07b6a9c00745c656e75dd"},
-    {file = "backports.cached_property-1.0.2-py3-none-any.whl", hash = "sha256:baeb28e1cd619a3c9ab8941431fe34e8490861fb998c6c4590693d50171db0cc"},
+    {file = "backcall-0.2.0-py2.py3-none-any.whl", hash = "sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255"},
+    {file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"},
 ]
 
 [[package]]
@@ -402,6 +404,18 @@ d = ["aiohttp (>=3.7.4)"]
 jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
 uvloop = ["uvloop (>=0.15.2)"]
 
+[[package]]
+name = "braceexpand"
+version = "0.1.7"
+description = "Bash-style brace expansion for Python"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
+    {file = "braceexpand-0.1.7-py2.py3-none-any.whl", hash = "sha256:91332d53de7828103dcae5773fb43bc34950b0c8160e35e0f44c4427a3b85014"},
+    {file = "braceexpand-0.1.7.tar.gz", hash = "sha256:e6e539bd20eaea53547472ff94f4fb5c3d3bf9d0a89388c4b56663aba765f705"},
+]
+
 [[package]]
 name = "brotli"
 version = "1.0.9"
@@ -750,24 +764,6 @@ files = [
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
-[[package]]
-name = "cmaes"
-version = "0.9.1"
-description = "Lightweight Covariance Matrix Adaptation Evolution Strategy (CMA-ES) implementation for Python 3."
-category = "main"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "cmaes-0.9.1-py3-none-any.whl", hash = "sha256:6e2930b6a99dd94621bf62966c13d29e6a7f90a909b4e4266010d5f3a7fb74b8"},
-    {file = "cmaes-0.9.1.tar.gz", hash = "sha256:d122f8d46377f643a150c85ffc81c4e33909a34cfdcb522ee7a6fb17ea4f232c"},
-]
-
-[package.dependencies]
-numpy = "*"
-
-[package.extras]
-cmawm = ["scipy"]
-
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -799,19 +795,24 @@ humanfriendly = ">=9.1"
 cron = ["capturer (>=2.4)"]
 
 [[package]]
-name = "colorlog"
-version = "6.7.0"
-description = "Add colours to the output of Python's logging module."
+name = "comm"
+version = "0.1.3"
+description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
 category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "colorlog-6.7.0-py2.py3-none-any.whl", hash = "sha256:0d33ca236784a1ba3ff9c532d4964126d8a2c44f1f0cb1d2b0728196f512f662"},
-    {file = "colorlog-6.7.0.tar.gz", hash = "sha256:bd94bd21c1e13fac7bd3153f4bc3a7dc0eb0974b8bc2fdf1a989e474f6e582e5"},
+    {file = "comm-0.1.3-py3-none-any.whl", hash = "sha256:16613c6211e20223f215fc6d3b266a247b6e2641bf4e0a3ad34cb1aff2aa3f37"},
+    {file = "comm-0.1.3.tar.gz", hash = "sha256:a61efa9daffcfbe66fd643ba966f846a624e4e6d6767eda9cf6e993aadaab93e"},
 ]
 
+[package.dependencies]
+traitlets = ">=5.3"
+
 [package.extras]
-development = ["black", "flake8", "mypy", "pytest", "types-colorama"]
+lint = ["black (>=22.6.0)", "mdformat (>0.7)", "mdformat-gfm (>=0.3.5)", "ruff (>=0.0.156)"]
+test = ["pytest"]
+typing = ["mypy (>=0.990)"]
 
 [[package]]
 name = "contourpy"
@@ -890,63 +891,63 @@ test-no-images = ["pytest"]
 
 [[package]]
 name = "coverage"
-version = "7.2.3"
+version = "7.2.5"
 description = "Code coverage measurement for Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
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+
 [[package]]
 name = "darglint"
 version = "1.8.1"
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 ]
 
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+optional = false
+python-versions = ">=3.7"
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+
 [[package]]
 name = "decorator"
 version = "5.1.1"
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 ]
 
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+description = "Utilities for comparing sequences"
+category = "main"
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+python-versions = "*"
+files = [
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+
 [[package]]
 name = "distlib"
 version = "0.3.6"
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 ]
 
+[[package]]
+name = "docker-pycreds"
+version = "0.4.0"
+description = "Python bindings for the docker credentials store API"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
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+
+[package.dependencies]
+six = ">=1.4.0"
+
 [[package]]
 name = "docopt"
 version = "0.6.2"
@@ -1053,15 +1158,91 @@ files = [
 ]
 
 [[package]]
-name = "einops"
-version = "0.3.2"
-description = "A new flavour of deep learning operations"
+name = "editdistance"
+version = "0.6.2"
+description = "Fast implementation of the edit distance(Levenshtein distance)"
 category = "main"
 optional = false
-python-versions = "*"
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 [package.extras]
 test = ["pytest (>=6)"]
 
+[[package]]
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+
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+version = "0.95.1"
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@@ -1144,19 +1340,19 @@ dev = ["Sphinx (==2.1.0)", "future (==0.17.1)", "numpy (==1.16.4)", "pytest (==4
 
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 [[package]]
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 ]
 
+[[package]]
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+description = "A Simple Python Module for English Grapheme To Phoneme Conversion"
+category = "main"
+optional = false
+python-versions = "*"
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+numpy = ">=1.13.1"
+
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 description = "Git Object Database"
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 python-versions = ">=3.7"
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 version = "3.1.31"
 description = "GitPython is a Python library used to interact with Git repositories"
-category = "dev"
+category = "main"
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 python-versions = ">=3.7"
 files = [
@@ -1431,14 +1645,14 @@ gitdb = ">=4.0.1,<5"
 
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 [package.dependencies]
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 [package.extras]
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-    {file = "hmmlearn-0.2.8-cp38-cp38-macosx_10_15_x86_64.whl", hash = "sha256:40b29bbbf2ea8441d1ea1cb7b1cf04f1d0cf3d8b3486cfa1b57f9d98d4c38cbe"},
-    {file = "hmmlearn-0.2.8-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d7add09c9c30c221eea9eade492441c6d707177220dc8b1ee04e4ebe49f51c1b"},
-    {file = "hmmlearn-0.2.8-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:93d16582ca8be996beb361791d3620ae9566904ff12dd556dc5f88a2158691cc"},
-    {file = "hmmlearn-0.2.8-cp38-cp38-win_amd64.whl", hash = "sha256:63cb380ce707ab7bc22ebf76cb095b28224363a63ba4ccb6a61c3237005bea32"},
-    {file = "hmmlearn-0.2.8-cp39-cp39-macosx_10_15_x86_64.whl", hash = "sha256:dec1916bab77108c36cefa3ee5baca72ced6ba1f3673a52672046a63a2b0dba6"},
-    {file = "hmmlearn-0.2.8-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a9eb2cd5647cdb96085fe4108705dd0e34f9eab43fa3ac50079d25d8df87bd44"},
-    {file = "hmmlearn-0.2.8-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:127097d6be73880ac4b9cb6e4d11fd8ab3028827ea62a826647cbfe6de5955e6"},
-    {file = "hmmlearn-0.2.8-cp39-cp39-win_amd64.whl", hash = "sha256:4cf3e6de054b2a7ec2d75946e5b1cd3c3485c0b60e9b7de81374b5b8703f2387"},
-    {file = "hmmlearn-0.2.8.tar.gz", hash = "sha256:696931e3dce6801cc9b78c629f543061dd7eb67a71155b03d1b39ea172a2a5f9"},
-]
-
-[package.dependencies]
-numpy = ">=1.10"
-scikit-learn = ">=0.16"
-scipy = ">=0.19"
-
-[package.extras]
-docs = ["matplotlib", "pydata-sphinx-theme", "sphinx (>=2.0)", "sphinx-gallery"]
-tests = ["pytest"]
-
 [[package]]
 name = "httpcore"
 version = "0.17.0"
@@ -1705,18 +1805,19 @@ socks = ["socksio (>=1.0.0,<2.0.0)"]
 
 [[package]]
 name = "huggingface-hub"
-version = "0.13.4"
+version = "0.14.1"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 category = "main"
 optional = false
 python-versions = ">=3.7.0"
 files = [
-    {file = "huggingface_hub-0.13.4-py3-none-any.whl", hash = "sha256:4d3d40593de6673d624a4baaaf249b9bf5165bfcafd1ad58de361931f0b4fda5"},
-    {file = "huggingface_hub-0.13.4.tar.gz", hash = "sha256:db83d9c2f76aed8cf49893ffadd6be24e82074da2f64b1d36b8ba40eb255e115"},
+    {file = "huggingface_hub-0.14.1-py3-none-any.whl", hash = "sha256:9fc619170d800ff3793ad37c9757c255c8783051e1b5b00501205eb43ccc4f27"},
+    {file = "huggingface_hub-0.14.1.tar.gz", hash = "sha256:9ab899af8e10922eac65e290d60ab956882ab0bf643e3d990b1394b6b47b7fbc"},
 ]
 
 [package.dependencies]
 filelock = "*"
+fsspec = "*"
 packaging = ">=20.9"
 pyyaml = ">=5.1"
 requests = "*"
@@ -1724,13 +1825,13 @@ tqdm = ">=4.42.1"
 typing-extensions = ">=3.7.4.3"
 
 [package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "black (>=23.1,<24.0)", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "black (>=23.1,<24.0)", "gradio", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
 cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "black (>=23.1,<24.0)", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "black (>=23.1,<24.0)", "gradio", "jedi", "mypy (==0.982)", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
 quality = ["black (>=23.1,<24.0)", "mypy (==0.982)", "ruff (>=0.0.241)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "jedi", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "soundfile"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "gradio", "jedi", "pytest", "pytest-cov", "pytest-env", "pytest-xdist", "soundfile"]
 torch = ["torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"]
 
@@ -1750,31 +1851,32 @@ files = [
 pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_version >= \"3.8\""}
 
 [[package]]
-name = "hyperpyyaml"
+name = "hydra-core"
 version = "1.2.0"
-description = "Extensions to YAML syntax for better python interaction"
+description = "A framework for elegantly configuring complex applications"
 category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "HyperPyYAML-1.2.0-py3-none-any.whl", hash = "sha256:ba54decb0b1680304df9aec6f5c0809bcc17178c6c1ad369cfb9725557acb965"},
-    {file = "HyperPyYAML-1.2.0.tar.gz", hash = "sha256:658dfbbfc5788c92fa27843c643bedbd468220e0f25b47c00dabf80f3bc7e1d7"},
+    {file = "hydra-core-1.2.0.tar.gz", hash = "sha256:4990721ce4ac69abafaffee566d6b63a54faa6501ecce65b338d3251446ff634"},
+    {file = "hydra_core-1.2.0-py3-none-any.whl", hash = "sha256:b6614fd6d6a97a9499f7ddbef02c9dd38f2fec6a9bc83c10e248db1dae50a528"},
 ]
 
 [package.dependencies]
-pyyaml = ">=5.1"
-"ruamel.yaml" = ">=0.17.8"
+antlr4-python3-runtime = ">=4.9.0,<4.10.0"
+omegaconf = ">=2.2,<3.0"
+packaging = "*"
 
 [[package]]
 name = "identify"
-version = "2.5.22"
+version = "2.5.24"
 description = "File identification library for Python"
 category = "dev"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "identify-2.5.22-py2.py3-none-any.whl", hash = "sha256:f0faad595a4687053669c112004178149f6c326db71ee999ae4636685753ad2f"},
-    {file = "identify-2.5.22.tar.gz", hash = "sha256:f7a93d6cf98e29bd07663c60728e7a4057615068d7a639d132dc883b2d54d31e"},
+    {file = "identify-2.5.24-py2.py3-none-any.whl", hash = "sha256:986dbfb38b1140e763e413e6feb44cd731faf72d1909543178aa79b0e258265d"},
+    {file = "identify-2.5.24.tar.gz", hash = "sha256:0aac67d5b4812498056d28a9a512a483f5085cc28640b02b258a59dac34301d4"},
 ]
 
 [package.extras]
@@ -1794,14 +1896,14 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "6.3.0"
+version = "6.6.0"
 description = "Read metadata from Python packages"
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "importlib_metadata-6.3.0-py3-none-any.whl", hash = "sha256:8f8bd2af397cf33bd344d35cfe7f489219b7d14fc79a3f854b75b8417e9226b0"},
-    {file = "importlib_metadata-6.3.0.tar.gz", hash = "sha256:23c2bcae4762dfb0bbe072d358faec24957901d75b6c4ab11172c0c982532402"},
+    {file = "importlib_metadata-6.6.0-py3-none-any.whl", hash = "sha256:43dd286a2cd8995d5eaef7fee2066340423b818ed3fd70adf0bad5f1fac53fed"},
+    {file = "importlib_metadata-6.6.0.tar.gz", hash = "sha256:92501cdf9cc66ebd3e612f1b4f0c0765dfa42f0fa38ffb319b6bd84dd675d705"},
 ]
 
 [package.dependencies]
@@ -1831,6 +1933,25 @@ zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
 
+[[package]]
+name = "inflect"
+version = "6.0.4"
+description = "Correctly generate plurals, singular nouns, ordinals, indefinite articles; convert numbers to words"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "inflect-6.0.4-py3-none-any.whl", hash = "sha256:2d592e7e4eafb6e51f9c626c5dd4288f5ce55981eaac9b342e868ead95ead5c3"},
+    {file = "inflect-6.0.4.tar.gz", hash = "sha256:1842649a17b6cad66812a5c9bdfacb6310e1e7b6dd8a31f026766df1b62612eb"},
+]
+
+[package.dependencies]
+pydantic = ">=1.9.1"
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["flake8 (<5)", "pygments", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
+
 [[package]]
 name = "iniconfig"
 version = "2.0.0"
@@ -1843,6 +1964,102 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
+[[package]]
+name = "ipykernel"
+version = "6.22.0"
+description = "IPython Kernel for Jupyter"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "ipykernel-6.22.0-py3-none-any.whl", hash = "sha256:1ae6047c1277508933078163721bbb479c3e7292778a04b4bacf0874550977d6"},
+    {file = "ipykernel-6.22.0.tar.gz", hash = "sha256:302558b81f1bc22dc259fb2a0c5c7cf2f4c0bdb21b50484348f7bafe7fb71421"},
+]
+
+[package.dependencies]
+appnope = {version = "*", markers = "platform_system == \"Darwin\""}
+comm = ">=0.1.1"
+debugpy = ">=1.6.5"
+ipython = ">=7.23.1"
+jupyter-client = ">=6.1.12"
+jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
+matplotlib-inline = ">=0.1"
+nest-asyncio = "*"
+packaging = "*"
+psutil = "*"
+pyzmq = ">=20"
+tornado = ">=6.1"
+traitlets = ">=5.4.0"
+
+[package.extras]
+cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
+pyqt5 = ["pyqt5"]
+pyside6 = ["pyside6"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov", "pytest-timeout"]
+
+[[package]]
+name = "ipython"
+version = "8.13.2"
+description = "IPython: Productive Interactive Computing"
+category = "main"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "ipython-8.13.2-py3-none-any.whl", hash = "sha256:ffca270240fbd21b06b2974e14a86494d6d29290184e788275f55e0b55914926"},
+    {file = "ipython-8.13.2.tar.gz", hash = "sha256:7dff3fad32b97f6488e02f87b970f309d082f758d7b7fc252e3b19ee0e432dbb"},
+]
+
+[package.dependencies]
+appnope = {version = "*", markers = "sys_platform == \"darwin\""}
+backcall = "*"
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+decorator = "*"
+jedi = ">=0.16"
+matplotlib-inline = "*"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
+pickleshare = "*"
+prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
+pygments = ">=2.4.0"
+stack-data = "*"
+traitlets = ">=5"
+typing-extensions = {version = "*", markers = "python_version < \"3.10\""}
+
+[package.extras]
+all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+black = ["black"]
+doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+kernel = ["ipykernel"]
+nbconvert = ["nbconvert"]
+nbformat = ["nbformat"]
+notebook = ["ipywidgets", "notebook"]
+parallel = ["ipyparallel"]
+qtconsole = ["qtconsole"]
+test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
+test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
+
+[[package]]
+name = "ipywidgets"
+version = "8.0.6"
+description = "Jupyter interactive widgets"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "ipywidgets-8.0.6-py3-none-any.whl", hash = "sha256:a60bf8d2528997e05ac83fd19ea2fbe65f2e79fbe1b2b35779bdfc46c2941dcc"},
+    {file = "ipywidgets-8.0.6.tar.gz", hash = "sha256:de7d779f2045d60de9f6c25f653fdae2dba57898e6a1284494b3ba20b6893bb8"},
+]
+
+[package.dependencies]
+ipykernel = ">=4.5.1"
+ipython = ">=6.1.0"
+jupyterlab-widgets = ">=3.0.7,<3.1.0"
+traitlets = ">=4.3.1"
+widgetsnbextension = ">=4.0.7,<4.1.0"
+
+[package.extras]
+test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
+
 [[package]]
 name = "isort"
 version = "5.12.0"
@@ -1861,6 +2078,41 @@ pipfile-deprecated-finder = ["pip-shims (>=0.5.2)", "pipreqs", "requirementslib"
 plugins = ["setuptools"]
 requirements-deprecated-finder = ["pip-api", "pipreqs"]
 
+[[package]]
+name = "jedi"
+version = "0.18.2"
+description = "An autocompletion tool for Python that can be used for text editors."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "jedi-0.18.2-py2.py3-none-any.whl", hash = "sha256:203c1fd9d969ab8f2119ec0a3342e0b49910045abe6af0a3ae83a5764d54639e"},
+    {file = "jedi-0.18.2.tar.gz", hash = "sha256:bae794c30d07f6d910d32a7048af09b5a39ed740918da923c6b780790ebac612"},
+]
+
+[package.dependencies]
+parso = ">=0.8.0,<0.9.0"
+
+[package.extras]
+docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
+qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
+testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+
+[[package]]
+name = "jiwer"
+version = "2.3.0"
+description = "Evaluate your speech-to-text system with similarity measures such as word error rate (WER)"
+category = "main"
+optional = false
+python-versions = ">=3.6.2,<4.0.0"
+files = [
+    {file = "jiwer-2.3.0-py3-none-any.whl", hash = "sha256:ef972f8e4feb1b28402d0c0a4350a051e7b41c90119eb1e99d46b4a2002d512f"},
+    {file = "jiwer-2.3.0.tar.gz", hash = "sha256:58cc41d96a1982761114c012b6d559cd9bb092b8e09e76997da168dda3286cbd"},
+]
+
+[package.dependencies]
+python-Levenshtein = "0.12.2"
+
 [[package]]
 name = "joblib"
 version = "1.2.0"
@@ -1874,21 +2126,90 @@ files = [
 ]
 
 [[package]]
-name = "julius"
-version = "0.2.7"
-description = "Nice DSP sweets: resampling, FFT Convolutions. All with PyTorch, differentiable and with CUDA support."
+name = "jupyter-client"
+version = "8.2.0"
+description = "Jupyter protocol implementation and client libraries"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter_client-8.2.0-py3-none-any.whl", hash = "sha256:b18219aa695d39e2ad570533e0d71fb7881d35a873051054a84ee2a17c4b7389"},
+    {file = "jupyter_client-8.2.0.tar.gz", hash = "sha256:9fe233834edd0e6c0aa5f05ca2ab4bdea1842bfd2d8a932878212fc5301ddaf0"},
+]
+
+[package.dependencies]
+importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
+jupyter-core = ">=4.12,<5.0.0 || >=5.1.0"
+python-dateutil = ">=2.8.2"
+pyzmq = ">=23.0"
+tornado = ">=6.2"
+traitlets = ">=5.3"
+
+[package.extras]
+docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
+
+[[package]]
+name = "jupyter-core"
+version = "5.3.0"
+description = "Jupyter core package. A base package on which Jupyter projects rely."
 category = "main"
 optional = false
-python-versions = ">=3.6.0"
+python-versions = ">=3.8"
 files = [
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@@ -1969,111 +2290,242 @@ files = [
 ]
 
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+
+[[package]]
+name = "loguru"
+version = "0.7.0"
+description = "Python logging made (stupidly) simple"
+category = "main"
+optional = false
+python-versions = ">=3.5"
+files = [
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 ]
 
 [package.dependencies]
-MarkupSafe = ">=0.9.2"
+colorama = {version = ">=0.3.4", markers = "sys_platform == \"win32\""}
+win32-setctime = {version = ">=1.0.0", markers = "sys_platform == \"win32\""}
 
 [package.extras]
-babel = ["Babel"]
-lingua = ["lingua"]
-testing = ["pytest"]
+dev = ["Sphinx (==5.3.0)", "colorama (==0.4.5)", "colorama (==0.4.6)", "freezegun (==1.1.0)", "freezegun (==1.2.2)", "mypy (==v0.910)", "mypy (==v0.971)", "mypy (==v0.990)", "pre-commit (==3.2.1)", "pytest (==6.1.2)", "pytest (==7.2.1)", "pytest-cov (==2.12.1)", "pytest-cov (==4.0.0)", "pytest-mypy-plugins (==1.10.1)", "pytest-mypy-plugins (==1.9.3)", "sphinx-autobuild (==2021.3.14)", "sphinx-rtd-theme (==1.2.0)", "tox (==3.27.1)", "tox (==4.4.6)"]
 
 [[package]]
 name = "markdown"
@@ -2097,7 +2549,7 @@ testing = ["coverage", "pyyaml"]
 name = "markdown-it-py"
 version = "2.2.0"
 description = "Python port of markdown-it. Markdown parsing, done right!"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2178,6 +2630,27 @@ files = [
     {file = "MarkupSafe-2.1.2.tar.gz", hash = "sha256:abcabc8c2b26036d62d4c746381a6f7cf60aafcc653198ad678306986b09450d"},
 ]
 
+[[package]]
+name = "marshmallow"
+version = "3.19.0"
+description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
+[package.dependencies]
+packaging = ">=17.0"
+
+[package.extras]
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+docs = ["alabaster (==0.7.12)", "autodocsumm (==0.2.9)", "sphinx (==5.3.0)", "sphinx-issues (==3.0.1)", "sphinx-version-warning (==1.1.2)"]
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+tests = ["pytest", "pytz", "simplejson"]
+
 [[package]]
 name = "matplotlib"
 version = "3.7.1"
@@ -2241,6 +2714,21 @@ pillow = ">=6.2.0"
 pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 
+[[package]]
+name = "matplotlib-inline"
+version = "0.1.6"
+description = "Inline Matplotlib backend for Jupyter"
+category = "main"
+optional = false
+python-versions = ">=3.5"
+files = [
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+]
+
+[package.dependencies]
+traitlets = "*"
+
 [[package]]
 name = "mccabe"
 version = "0.7.0"
@@ -2257,7 +2745,7 @@ files = [
 name = "mdurl"
 version = "0.1.2"
 description = "Markdown URL utilities"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
@@ -2283,6 +2771,79 @@ docs = ["sphinx"]
 gmpy = ["gmpy2 (>=2.1.0a4)"]
 tests = ["pytest (>=4.6)"]
 
+[[package]]
+name = "msgpack"
+version = "1.0.5"
+description = "MessagePack serializer"
+category = "main"
+optional = false
+python-versions = "*"
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+
 [[package]]
 name = "multidict"
 version = "6.0.4"
@@ -2392,23 +2953,111 @@ files = [
 ]
 
 [[package]]
-name = "networkx"
-version = "2.8.8"
-description = "Python package for creating and manipulating graphs and networks"
+name = "nemo-toolkit"
+version = "1.17.0"
+description = "NeMo - a toolkit for Conversational AI"
 category = "main"
 optional = false
-python-versions = ">=3.8"
+python-versions = "*"
+files = [
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+editdistance = {version = "*", optional = true, markers = "extra == \"asr\""}
+g2p-en = {version = "*", optional = true, markers = "extra == \"asr\""}
+huggingface-hub = "*"
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+webdataset = {version = ">=0.1.48,<=0.1.62", optional = true, markers = "extra == \"asr\""}
+wget = "*"
+wrapt = "*"
+youtokentome = {version = ">=1.0.5", optional = true, markers = "extra == \"asr\""}
+
+[package.extras]
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+test = ["attrdict", "einops", "hydra-core (>=1.2.0,<1.3)", "inflect", "jieba", "kornia", "librosa", "matplotlib", "nemo-text-processing", "nltk", "omegaconf (>=2.2,<2.3)", "pandas", "pypinyin", "pypinyin-dict", "pytorch-lightning (>=1.9.0,<=1.9.4)", "pyyaml (<6)", "sacremoses (>=0.0.43)", "sentencepiece (<1.0.0)", "torchmetrics (>=0.11.0)", "transformers (>=4.0.1)", "wandb", "webdataset (>=0.1.48,<=0.1.62)", "youtokentome (>=1.0.5)"]
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+
+[[package]]
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+version = "1.5.6"
+description = "Patch asyncio to allow nested event loops"
+category = "main"
+optional = false
+python-versions = ">=3.5"
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+plot = ["matplotlib"]
+tgrep = ["pyparsing"]
+twitter = ["twython"]
 
 [[package]]
 name = "nodeenv"
@@ -2427,46 +3076,41 @@ setuptools = "*"
 
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 description = "compiling Python code using LLVM"
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-setuptools = "*"
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 [[package]]
 name = "numpy"
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 [[package]]
 name = "omegaconf"
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 description = "A flexible configuration library"
 category = "main"
 optional = false
 python-versions = ">=3.6"
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 antlr4-python3-runtime = ">=4.9.0,<4.10.0"
 PyYAML = ">=5.1.0"
 
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+
+[package.extras]
+lint = ["lintrunner (>=0.10.0)", "lintrunner-adapters (>=0.3)"]
+
 [[package]]
 name = "onnxruntime"
 version = "1.14.1"
@@ -2647,81 +3340,51 @@ packaging = "*"
 protobuf = "*"
 sympy = "*"
 
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 [[package]]
 name = "packaging"
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 description = "Core utilities for Python packages"
 category = "main"
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 python-versions = ">=3.7"
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 category = "main"
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 python-versions = ">=3.8"
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+    {file = "pandas-2.0.1.tar.gz", hash = "sha256:19b8e5270da32b41ebf12f0e7165efa7024492e9513fb46fb631c5022ae5709d"},
 ]
 
 [package.dependencies]
@@ -2753,6 +3416,22 @@ sql-other = ["SQLAlchemy (>=1.4.16)"]
 test = ["hypothesis (>=6.34.2)", "pytest (>=7.0.0)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"]
 xml = ["lxml (>=4.6.3)"]
 
+[[package]]
+name = "parso"
+version = "0.8.3"
+description = "A Python Parser"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "parso-0.8.3-py2.py3-none-any.whl", hash = "sha256:c001d4636cd3aecdaf33cbb40aebb59b094be2a74c556778ef5576c175e19e75"},
+    {file = "parso-0.8.3.tar.gz", hash = "sha256:8c07be290bb59f03588915921e29e8a50002acaf2cdc5fa0e0114f91709fafa0"},
+]
+
+[package.extras]
+qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
+testing = ["docopt", "pytest (<6.0.0)"]
+
 [[package]]
 name = "pathspec"
 version = "0.11.1"
@@ -2765,6 +3444,17 @@ files = [
     {file = "pathspec-0.11.1.tar.gz", hash = "sha256:2798de800fa92780e33acca925945e9a19a133b715067cf165b8866c15a31687"},
 ]
 
+[[package]]
+name = "pathtools"
+version = "0.1.2"
+description = "File system general utilities"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
+    {file = "pathtools-0.1.2.tar.gz", hash = "sha256:7c35c5421a39bb82e58018febd90e3b6e5db34c5443aaaf742b3f33d4655f1c0"},
+]
+
 [[package]]
 name = "pbr"
 version = "5.11.1"
@@ -2777,6 +3467,33 @@ files = [
     {file = "pbr-5.11.1.tar.gz", hash = "sha256:aefc51675b0b533d56bb5fd1c8c6c0522fe31896679882e1c4c63d5e4a0fccb3"},
 ]
 
+[[package]]
+name = "pexpect"
+version = "4.8.0"
+description = "Pexpect allows easy control of interactive console applications."
+category = "main"
+optional = false
+python-versions = "*"
+files = [
+    {file = "pexpect-4.8.0-py2.py3-none-any.whl", hash = "sha256:0b48a55dcb3c05f3329815901ea4fc1537514d6ba867a152b581d69ae3710937"},
+    {file = "pexpect-4.8.0.tar.gz", hash = "sha256:fc65a43959d153d0114afe13997d439c22823a27cefceb5ff35c2178c6784c0c"},
+]
+
+[package.dependencies]
+ptyprocess = ">=0.5"
+
+[[package]]
+name = "pickleshare"
+version = "0.7.5"
+description = "Tiny 'shelve'-like database with concurrency support"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
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+]
+
 [[package]]
 name = "pillow"
 version = "9.5.0"
@@ -2807,6 +3524,7 @@ files = [
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@@ -2856,21 +3574,33 @@ files = [
 docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
 
+[[package]]
+name = "plac"
+version = "1.3.5"
+description = "The smartest command line arguments parser in the world"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
+    {file = "plac-1.3.5-py2.py3-none-any.whl", hash = "sha256:a8933d21a40fe2cec177a2f96217425a4e889d275aa3e25ecf9a9640ab16d416"},
+    {file = "plac-1.3.5.tar.gz", hash = "sha256:38bdd864d0450fb748193aa817b9c458a8f5319fbf97b2261151cfc0a5812090"},
+]
+
 [[package]]
 name = "platformdirs"
-version = "3.2.0"
+version = "3.5.0"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "platformdirs-3.5.0-py3-none-any.whl", hash = "sha256:47692bc24c1958e8b0f13dd727307cff1db103fca36399f457da8e05f222fdc4"},
+    {file = "platformdirs-3.5.0.tar.gz", hash = "sha256:7954a68d0ba23558d753f73437c55f89027cf8f5108c19844d4b82e5af396335"},
 ]
 
 [package.extras]
-docs = ["furo (>=2022.12.7)", "proselint (>=0.13)", "sphinx (>=6.1.3)", "sphinx-autodoc-typehints (>=1.22,!=1.23.4)"]
-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.2.2)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"]
+docs = ["furo (>=2023.3.27)", "proselint (>=0.13)", "sphinx (>=6.1.3)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.3.1)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"]
 
 [[package]]
 name = "pluggy"
@@ -2890,19 +3620,19 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "pooch"
-version = "1.7.0"
+version = "1.6.0"
 description = "\"Pooch manages your Python library's sample data files: it automatically downloads and stores them in a local directory, with support for versioning and corruption checks.\""
 category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.6"
 files = [
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+    {file = "pooch-1.6.0-py3-none-any.whl", hash = "sha256:3bf0e20027096836b8dbce0152dbb785a269abeb621618eb4bdd275ff1e23c9c"},
+    {file = "pooch-1.6.0.tar.gz", hash = "sha256:57d20ec4b10dd694d2b05bb64bc6b109c6e85a6c1405794ce87ed8b341ab3f44"},
 ]
 
 [package.dependencies]
+appdirs = ">=1.3.0"
 packaging = ">=20.0"
-platformdirs = ">=2.5.0"
 requests = ">=2.19.0"
 
 [package.extras]
@@ -2912,14 +3642,14 @@ xxhash = ["xxhash (>=1.4.3)"]
 
 [[package]]
 name = "pre-commit"
-version = "3.2.2"
+version = "3.3.1"
 description = "A framework for managing and maintaining multi-language pre-commit hooks."
 category = "dev"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pre_commit-3.2.2-py2.py3-none-any.whl", hash = "sha256:0b4210aea813fe81144e87c5a291f09ea66f199f367fa1df41b55e1d26e1e2b4"},
-    {file = "pre_commit-3.2.2.tar.gz", hash = "sha256:5b808fcbda4afbccf6d6633a56663fed35b6c2bc08096fd3d47ce197ac351d9d"},
+    {file = "pre_commit-3.3.1-py2.py3-none-any.whl", hash = "sha256:218e9e3f7f7f3271ebc355a15598a4d3893ad9fc7b57fe446db75644543323b9"},
+    {file = "pre_commit-3.3.1.tar.gz", hash = "sha256:733f78c9a056cdd169baa6cd4272d51ecfda95346ef8a89bf93712706021b907"},
 ]
 
 [package.dependencies]
@@ -2946,109 +3676,112 @@ files = [
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
 
 [[package]]
-name = "primepy"
-version = "1.3"
-description = "This module contains several useful functions to work with prime numbers. from primePy import primes"
+name = "prompt-toolkit"
+version = "3.0.38"
+description = "Library for building powerful interactive command lines in Python"
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7.0"
 files = [
-    {file = "primePy-1.3-py3-none-any.whl", hash = "sha256:5ed443718765be9bf7e2ff4c56cdff71b42140a15b39d054f9d99f0009e2317a"},
-    {file = "primePy-1.3.tar.gz", hash = "sha256:25fd7e25344b0789a5984c75d89f054fcf1f180bef20c998e4befbac92de4669"},
+    {file = "prompt_toolkit-3.0.38-py3-none-any.whl", hash = "sha256:45ea77a2f7c60418850331366c81cf6b5b9cf4c7fd34616f733c5427e6abbb1f"},
+    {file = "prompt_toolkit-3.0.38.tar.gz", hash = "sha256:23ac5d50538a9a38c8bde05fecb47d0b403ecd0662857a86f886f798563d5b9b"},
 ]
 
+[package.dependencies]
+wcwidth = "*"
+
 [[package]]
 name = "protobuf"
-version = "3.20.1"
-description = "Protocol Buffers"
+version = "4.22.4"
+description = ""
 category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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-torchaudio = ">=0.10,<1.0"
-torchmetrics = ">=0.6,<1.0"
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+[package.extras]
+test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
+
+[[package]]
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+category = "main"
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+python-versions = "*"
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+[[package]]
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-interactive = ["prodigy (>=1.11.0)"]
-testing = ["jupyter", "papermill", "pytest (>=6.0)", "pytest-cov (>=2.10)"]
+tests = ["pytest"]
 
 [[package]]
 name = "pyannote-core"
-version = "4.5"
+version = "5.0.0"
 description = "Advanced data structures for handling temporal segments with attached labels."
 category = "main"
 optional = false
 python-versions = "*"
 files = [
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 scipy = ">=1.1"
-simplejson = ">=3.8.1"
 sortedcontainers = ">=2.0.4"
 typing-extensions = ">=3.7.4.1"
 
@@ -3059,14 +3792,14 @@ testing = ["flake8 (==3.7.9)", "pandas (>=0.17.1)", "pytest"]
 
 [[package]]
 name = "pyannote-database"
-version = "4.1.3"
+version = "5.0.1"
 description = "Interface to multimedia databases and experimental protocols"
 category = "main"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -3108,53 +3841,46 @@ docs = ["Sphinx (==2.2.2)", "ipython (==7.10.1)", "sphinx-rtd-theme (==0.4.3)"]
 tests = ["pytest"]
 
 [[package]]
-name = "pyannote-pipeline"
-version = "2.3"
-description = "Tunable pipelines."
+name = "pyasn1"
+version = "0.5.0"
+description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
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-[package.dependencies]
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-"pyannote.database" = ">=4.0"
-PyYAML = ">=3.12"
-scikit-learn = ">=0.20.2"
-tqdm = ">=4.29.1"
-
 [[package]]
-name = "pyasn1"
-version = "0.4.8"
-description = "ASN.1 types and codecs"
+name = "pyasn1-modules"
+version = "0.3.0"
+description = "A collection of ASN.1-based protocols modules"
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
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 ]
 
+[package.dependencies]
+pyasn1 = ">=0.4.6,<0.6.0"
+
 [[package]]
-name = "pyasn1-modules"
-version = "0.2.8"
-description = "A collection of ASN.1-based protocols modules."
+name = "pybind11"
+version = "2.10.4"
+description = "Seamless operability between C++11 and Python"
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.6"
 files = [
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 ]
 
-[package.dependencies]
-pyasn1 = ">=0.4.6,<0.5.0"
+[package.extras]
+global = ["pybind11-global (==2.10.4)"]
 
 [[package]]
 name = "pycodestyle"
@@ -3276,18 +4002,6 @@ typing-extensions = ">=4.2.0"
 dotenv = ["python-dotenv (>=0.10.4)"]
 email = ["email-validator (>=1.0.3)"]
 
-[[package]]
-name = "pydeprecate"
-version = "0.3.2"
-description = "Deprecation tooling"
-category = "main"
-optional = false
-python-versions = ">=3.6"
-files = [
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-]
-
 [[package]]
 name = "pydocstyle"
 version = "6.3.0"
@@ -3306,6 +4020,18 @@ snowballstemmer = ">=2.2.0"
 [package.extras]
 toml = ["tomli (>=1.2.3)"]
 
+[[package]]
+name = "pydub"
+version = "0.25.1"
+description = "Manipulate audio with an simple and easy high level interface"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
+    {file = "pydub-0.25.1-py2.py3-none-any.whl", hash = "sha256:65617e33033874b59d87db603aa1ed450633288aefead953b30bded59cb599a6"},
+    {file = "pydub-0.25.1.tar.gz", hash = "sha256:980a33ce9949cab2a569606b65674d748ecbca4f0796887fd6f46173a7b0d30f"},
+]
+
 [[package]]
 name = "pyflakes"
 version = "3.0.1"
@@ -3320,14 +4046,14 @@ files = [
 
 [[package]]
 name = "pygments"
-version = "2.15.0"
+version = "2.15.1"
 description = "Pygments is a syntax highlighting package written in Python."
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
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 [[package]]
 name = "requests"
-version = "2.28.2"
+version = "2.30.0"
 description = "Python HTTP for Humans."
 category = "main"
 optional = false
-python-versions = ">=3.7, <4"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
 certifi = ">=2017.4.17"
 charset-normalizer = ">=2,<4"
 idna = ">=2.5,<4"
-urllib3 = ">=1.21.1,<1.27"
+urllib3 = ">=1.21.1,<3"
 
 [package.extras]
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
@@ -3645,37 +4665,16 @@ requests = ">=2.0.0"
 [package.extras]
 rsa = ["oauthlib[signedtoken] (>=3.0.0)"]
 
-[[package]]
-name = "resampy"
-version = "0.4.2"
-description = "Efficient signal resampling"
-category = "main"
-optional = false
-python-versions = "*"
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-]
-
-[package.dependencies]
-numba = ">=0.53"
-numpy = ">=1.17"
-
-[package.extras]
-design = ["optuna (>=2.10.0)"]
-docs = ["numpydoc", "sphinx (!=1.3.1)"]
-tests = ["pytest (<8)", "pytest-cov", "scipy (>=1.0)"]
-
 [[package]]
 name = "rich"
-version = "13.3.3"
+version = "13.3.5"
 description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -3702,14 +4701,14 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruamel-yaml"
-version = "0.17.21"
+version = "0.17.22"
 description = "ruamel.yaml is a YAML parser/emitter that supports roundtrip preservation of comments, seq/map flow style, and map key order"
 category = "main"
 optional = false
 python-versions = ">=3"
 files = [
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 ]
 
 [package.dependencies]
@@ -3765,6 +4764,24 @@ files = [
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 ]
 
+[[package]]
+name = "sacremoses"
+version = "0.0.53"
+description = "SacreMoses"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
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+]
+
+[package.dependencies]
+click = "*"
+joblib = "*"
+regex = "*"
+six = "*"
+tqdm = "*"
+
 [[package]]
 name = "scikit-learn"
 version = "1.2.2"
@@ -3848,78 +4865,201 @@ doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (
 test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
-name = "semver"
-version = "2.13.0"
-description = "Python helper for Semantic Versioning (http://semver.org/)"
+name = "sentencepiece"
+version = "0.1.99"
+description = "SentencePiece python wrapper"
 category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+python-versions = "*"
 files = [
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 version = "5.0.0"
 description = "A pure Python implementation of a sliding window memory map manager"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.6"
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 numpy = ["numpy"]
 
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 [package.dependencies]
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 numpy = "*"
-packaging = "*"
-scipy = "*"
-sentencepiece = "*"
-torch = ">=1.9"
-torchaudio = "*"
-tqdm = "*"
+
+[package.extras]
+docs = ["linkify-it-py", "myst-parser", "sphinx", "sphinx-book-theme"]
+test = ["pytest"]
 
 [[package]]
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+description = "Extract data from python stack frames and tracebacks for informative displays"
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 [package.extras]
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-aiosqlite = ["aiosqlite", "greenlet (!=0.4.17)", "typing-extensions (!=3.10.0.1)"]
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-postgresql-psycopg2cffi = ["psycopg2cffi"]
-pymysql = ["pymysql"]
-sqlcipher = ["sqlcipher3-binary"]
+tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
 [[package]]
 name = "starlette"
@@ -4303,13 +5321,13 @@ widechars = ["wcwidth"]
 
 [[package]]
 name = "tensorboard"
-version = "2.12.1"
+version = "2.13.0"
 description = "TensorBoard lets you watch Tensors Flow"
 category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
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 requests = ">=2.21.0,<3"
 setuptools = ">=41.0.0"
 tensorboard-data-server = ">=0.7.0,<0.8.0"
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 wheel = ">=0.26"
 
@@ -4341,16 +5358,86 @@ files = [
 ]
 
 [[package]]
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-description = "What-If Tool TensorBoard plugin."
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+
+[package.extras]
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+
+[[package]]
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 python-versions = "*"
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 version = "3.1.0"
@@ -4483,78 +5570,6 @@ typing-extensions = "*"
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 opt-einsum = ["opt-einsum (>=3.3)"]
 
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 [[package]]
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 version = "4.65.0"
@@ -4602,28 +5638,114 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
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+
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 [[package]]
 name = "typing-extensions"
@@ -4651,31 +5773,32 @@ files = [
 
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 [[package]]
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+version = "0.22.0"
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 [package.dependencies]
@@ -4687,122 +5810,203 @@ standard = ["colorama (>=0.4)", "httptools (>=0.5.0)", "python-dotenv (>=0.13)",
 
 [[package]]
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+
+[[package]]
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+launch = ["awscli", "boto3", "botocore", "chardet", "google-auth", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "nbconvert", "nbformat", "typing-extensions"]
+media = ["bokeh", "moviepy", "numpy", "pillow", "plotly", "rdkit-pypi", "soundfile"]
+models = ["cloudpickle"]
+sweeps = ["sweeps (>=0.2.0)"]
+
+[[package]]
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+version = "0.2.6"
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+category = "main"
+optional = false
+python-versions = "*"
+files = [
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+[[package]]
+name = "webdataset"
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+python-versions = ">=3.6"
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+[package.dependencies]
+braceexpand = "*"
+numpy = "*"
 
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-version = "11.0.1"
+version = "11.0.2"
 description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)"
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 optional = false
 python-versions = ">=3.7"
 files = [
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+version = "2.3.3"
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 category = "main"
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 ]
 
 [package.dependencies]
 MarkupSafe = ">=2.1.1"
 
 [package.extras]
-watchdog = ["watchdog"]
+watchdog = ["watchdog (>=2.3)"]
+
+[[package]]
+name = "wget"
+version = "3.2"
+description = "pure python download utility"
+category = "main"
+optional = false
+python-versions = "*"
+files = [
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+]
 
 [[package]]
 name = "wheel"
@@ -4819,6 +6023,18 @@ files = [
 [package.extras]
 test = ["pytest (>=6.0.0)"]
 
+[[package]]
+name = "widgetsnbextension"
+version = "4.0.7"
+description = "Jupyter interactive widgets for Jupyter Notebook"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
 [[package]]
 name = "win32-setctime"
 version = "1.1.0"
@@ -4834,94 +6050,198 @@ files = [
 [package.extras]
 dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
 
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+    {file = "yarl-1.9.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b03917871bf859a81ccb180c9a2e6c1e04d2f6a51d953e6a5cdd70c93d4e5a2a"},
+    {file = "yarl-1.9.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c1012fa63eb6c032f3ce5d2171c267992ae0c00b9e164efe4d73db818465fac3"},
+    {file = "yarl-1.9.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a74dcbfe780e62f4b5a062714576f16c2f3493a0394e555ab141bf0d746bb955"},
+    {file = "yarl-1.9.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8c56986609b057b4839968ba901944af91b8e92f1725d1a2d77cbac6972b9ed1"},
+    {file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:2c315df3293cd521033533d242d15eab26583360b58f7ee5d9565f15fee1bef4"},
+    {file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:b7232f8dfbd225d57340e441d8caf8652a6acd06b389ea2d3222b8bc89cbfca6"},
+    {file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:53338749febd28935d55b41bf0bcc79d634881195a39f6b2f767870b72514caf"},
+    {file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:066c163aec9d3d073dc9ffe5dd3ad05069bcb03fcaab8d221290ba99f9f69ee3"},
+    {file = "yarl-1.9.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8288d7cd28f8119b07dd49b7230d6b4562f9b61ee9a4ab02221060d21136be80"},
+    {file = "yarl-1.9.2-cp39-cp39-win32.whl", hash = "sha256:b124e2a6d223b65ba8768d5706d103280914d61f5cae3afbc50fc3dfcc016623"},
+    {file = "yarl-1.9.2-cp39-cp39-win_amd64.whl", hash = "sha256:61016e7d582bc46a5378ffdd02cd0314fb8ba52f40f9cf4d9a5e7dbef88dee18"},
+    {file = "yarl-1.9.2.tar.gz", hash = "sha256:04ab9d4b9f587c06d801c2abfe9317b77cdf996c65a90d5e84ecc45010823571"},
 ]
 
 [package.dependencies]
 idna = ">=2.0"
 multidict = ">=4.0"
 
+[[package]]
+name = "youtokentome"
+version = "1.0.6"
+description = "Unsupervised text tokenizer focused on computational efficiency"
+category = "main"
+optional = false
+python-versions = ">=3.5.0"
+files = []
+develop = false
+
+[package.dependencies]
+Click = ">=7.0"
+
+[package.source]
+type = "git"
+url = "https://github.com/gbeckenkamp/YouTokenToMe.git"
+reference = "add_minimum_requirements"
+resolved_reference = "a2614d3e66cefd8d0fa73b23bc951feb98830090"
+
 [[package]]
 name = "yt-dlp"
 version = "2023.3.4"
@@ -4961,4 +6281,4 @@ testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.10"
-content-hash = "dd091800cf6f16a6519f70c4c368641f56a496fbad90f039d6ffc314a4bf723b"
+content-hash = "efa06dadefa851c813794e089e28c53f1217bdbc42e91ac47333db121f460ecd"
diff --git a/pyproject.toml b/pyproject.toml
index fda730c..720a9a9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,12 +9,13 @@ readme = "README.md"
 python = ">=3.9,<3.10"
 aiohttp = ">=3.8.4"
 aiofiles = ">=23.1.0"
+cython = ">=0.29.24"
 fastapi = ">=0.95.0"
 faster-whisper = ">=0.3.0"
 ffmpeg-python = ">=0.2.0"
 loguru = ">=0.6.0"
+nemo-toolkit = { version = ">=1.17.0", extras = ["asr"] }
 numpy = "==1.23.1"
-pyannote-audio = { version = ">=2.1.1", platform = "linux" }  # faced problems with pyannote.audio on Mac M1
 pydantic = ">=1.10.7"
 python-dotenv = ">=1.0.0"
 python-multipart = ">=0.0.6"
@@ -23,6 +24,7 @@ shortuuid = ">=1.0.0"
 torch = ">=1.9,<2.0"
 uvicorn = ">=0.21.1"
 yt-dlp = ">=2023.3.4"
+youtokentome = { git = "https://github.com/gbeckenkamp/YouTokenToMe.git", branch = "add_minimum_requirements" }
 
 [tool.poetry.group.test.dependencies]
 black = ">=23.1.0"
diff --git a/requirements.txt b/requirements.txt
index 1c58d96..4e7080c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,10 +1,11 @@
 aiohttp>=3.8.4
 aiofiles>=23.1.0
+cython>=0.29.24
 fastapi>=0.95.0
 faster-whisper>=0.3.0
 ffmpeg-python>=0.2.0
 loguru>=0.6.0
-pyannote-audio==2.1.1
+nemo-toolkit[asr]>=1.17.0
 pydantic>=1.10.7
 python-dotenv>=1.0.0
 python-multipart>=0.0.6
diff --git a/tests/test_config.py b/tests/test_config.py
index 19d4ec2..80fd737 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -13,21 +13,24 @@
 # limitations under the License.
 """Test config settings."""
 
-from wordcab_transcribe.config import Settings
+from wordcab_transcribe.config import settings
 
 
 def test_config() -> None:
     """Test default config settings with the .env file."""
-    assert Settings().project_name == "Wordcab Transcribe"
-    assert Settings().version == "0.1.0"
+    assert settings.project_name == "Wordcab Transcribe"
+    assert settings.version == "0.1.0"
     assert (
-        Settings().description
-        == "ASR FastAPI server using faster-whisper and pyannote-audio."
+        settings.description
+        == "💬 ASR FastAPI server using faster-whisper and NVIDIA NeMo."
     )
-    assert Settings().api_prefix == "/api/v1"
-    assert Settings().debug is True
-    assert Settings().batch_size == 1
-    assert Settings().max_wait == 0.1
-    assert Settings().whisper_model == "large-v2"
-    assert Settings().embeddings_model == "speechbrain/spkrec-ecapa-voxceleb"
-    assert Settings().compute_type == "int8_float16"
+    assert settings.api_prefix == "/api/v1"
+    assert settings.debug is True
+
+    assert settings.batch_size == 1
+    assert settings.max_wait == 0.1
+    assert settings.whisper_model == "large-v2"
+    assert settings.compute_type == "int8_float16"
+    assert settings.nemo_domain_type == "telephonic"
+    assert settings.nemo_storage_path == "nemo_storage"
+    assert settings.nemo_output_path == "nemo_outputs"
diff --git a/tests/test_models.py b/tests/test_models.py
index 084b0e5..6ef0ac1 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -30,12 +30,10 @@ def test_asr_response() -> None:
 def test_data_request_valid() -> None:
     """Test the DataRequest model with valid data."""
     data = {
-        "num_speakers": 2,
         "source_lang": "fr",
         "timestamps": "hms",
     }
     req = DataRequest(**data)
-    assert req.num_speakers == 2
     assert req.source_lang == "fr"
     assert req.timestamps == "hms"
 
@@ -43,16 +41,11 @@ def test_data_request_valid() -> None:
 def test_data_request_default() -> None:
     """Test the DataRequest model with default values."""
     req = DataRequest()
-    assert req.num_speakers == 0
     assert req.source_lang == "en"
-    assert req.timestamps == "seconds"
+    assert req.timestamps == "s"
 
 
 def test_data_request_invalid() -> None:
     """Test the DataRequest model with invalid data."""
-    with pytest.raises(ValueError, match="num_speakers must be a positive integer."):
-        DataRequest(num_speakers=-1)
-    with pytest.raises(
-        ValueError, match="timestamps must be one of 'seconds' or 'hms'."
-    ):
+    with pytest.raises(ValueError, match="timestamps must be one of 'hms', 'ms', 's'."):
         DataRequest(timestamps="invalid")
diff --git a/tests/utils/test_convert_functions.py b/tests/utils/test_convert_functions.py
new file mode 100644
index 0000000..e500fa3
--- /dev/null
+++ b/tests/utils/test_convert_functions.py
@@ -0,0 +1,61 @@
+# Copyright 2023 The Wordcab Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests the conversion functions."""
+from typing import Union
+
+import pytest
+
+from wordcab_transcribe.utils import (
+    _convert_ms_to_hms,
+    _convert_ms_to_s,
+    convert_timestamp,
+)
+
+
+@pytest.mark.parametrize(
+    "timestamp, target, expected",
+    [
+        (1000, "ms", 1000),
+        (1000, "s", 1),
+        (1000, "hms", "00:00:01.000"),
+        (3600000, "hms", "01:00:00.000"),
+        (3661000, "hms", "01:01:01.000"),
+    ],
+)
+def test_convert_timestamp(
+    timestamp: float, target: str, expected: Union[str, float]
+) -> None:
+    """Test the convert_timestamp function."""
+    assert convert_timestamp(timestamp, target) == expected
+
+
+def test_convert_timestamp_raises_error() -> None:
+    """Test the convert_timestamp function raises error."""
+    with pytest.raises(ValueError):
+        convert_timestamp(1000, "invalid_target")
+
+
+@pytest.mark.parametrize("ms, expected", [(1000, 1), (3600000, 3600), (3661000, 3661)])
+def test_convert_ms_to_s(ms: float, expected: float) -> None:
+    """Test the _convert_ms_to_s function."""
+    assert _convert_ms_to_s(ms) == expected
+
+
+@pytest.mark.parametrize(
+    "ms, expected",
+    [(1000, "00:00:01.000"), (3600000, "01:00:00.000"), (3661000, "01:01:01.000")],
+)
+def test_convert_ms_to_hms(ms: float, expected: str) -> None:
+    """Test the _convert_ms_to_hms function."""
+    assert _convert_ms_to_hms(ms) == expected
diff --git a/tests/utils/test_load_nemo_config.py b/tests/utils/test_load_nemo_config.py
new file mode 100644
index 0000000..07e3ece
--- /dev/null
+++ b/tests/utils/test_load_nemo_config.py
@@ -0,0 +1,39 @@
+# Copyright 2023 The Wordcab Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests the load_nemo_config function."""
+
+from pathlib import Path
+
+import pytest
+from omegaconf import OmegaConf
+
+from wordcab_transcribe.utils import load_nemo_config
+
+
+@pytest.mark.parametrize("domain_type", ["general", "meeting", "telephonic"])
+def test_load_nemo_config(domain_type: str):
+    """Test the load_nemo_config function."""
+    cfg = load_nemo_config(domain_type, "storage/path", "output/path")
+
+    cfg_path = f"config/nemo/diar_infer_{domain_type}.yaml"
+    with open(cfg_path) as f:
+        data = OmegaConf.load(f)
+
+    assert cfg != data
+
+    assert cfg.num_workers == 0
+    assert cfg.diarizer.manifest_filepath == str(
+        Path.cwd() / "storage/path/infer_manifest.json"
+    )
+    assert cfg.diarizer.out_dir == str(Path.cwd() / "output/path")
diff --git a/wordcab_transcribe/config.py b/wordcab_transcribe/config.py
index 1dd2549..eeb9f11 100644
--- a/wordcab_transcribe/config.py
+++ b/wordcab_transcribe/config.py
@@ -14,12 +14,16 @@
 """Configuration module of the Wordcab Transcribe."""
 
 from os import getenv
+from typing import Union
 
 from dotenv import load_dotenv
-from pydantic import BaseSettings
+from faster_whisper.utils import _MODELS
+from pydantic import validator
+from pydantic.dataclasses import dataclass
 
 
-class Settings(BaseSettings):
+@dataclass
+class Settings:
     """Configuration settings for the Wordcab Transcribe API."""
 
     # Basic API settings
@@ -33,21 +37,72 @@ class Settings(BaseSettings):
     max_wait: float
     # Model settings
     whisper_model: str
-    embeddings_model: str
     compute_type: str
+    nemo_domain_type: str
+    nemo_storage_path: str
+    nemo_output_path: str
+
+    @validator("project_name", "version", "description", "api_prefix")
+    def basic_parameters_must_not_be_none(
+        cls, value: str, field: str  # noqa: B902, N805
+    ):
+        """Check that the authentication parameters are not None."""
+        if value is None:
+            raise ValueError(
+                f"{field.name} must not be None, please verify the `.env` file."
+            )
+        return value
+
+    @validator("batch_size", "max_wait")
+    def batch_request_parameters_must_be_positive(
+        cls, value: Union[int, float], field: str  # noqa: B902, N805
+    ):
+        """Check that the model parameters are positive."""
+        if value <= 0:
+            raise ValueError(f"{field.name} must be positive.")
+        return value
+
+    @validator("whisper_model")
+    def whisper_model_must_be_valid(cls, value: str):  # noqa: B902, N805
+        """Check that the model name is valid."""
+        if value not in _MODELS:
+            raise ValueError(
+                f"{value} is not a valid model name. Choose one of {_MODELS}."
+            )
+        return value
+
+    @validator("compute_type")
+    def compute_type_must_be_valid(cls, value: str):  # noqa: B902, N805
+        """Check that the model precision is valid."""
+        if value not in {"int8", "int8_float16", "int16", "float_16"}:
+            raise ValueError(
+                f"{value} is not a valid compute type. Choose one of int8, int8_float16, int16, float_16."
+            )
+        return value
+
+    @validator("nemo_domain_type")
+    def nemo_domain_type_must_be_valid(cls, value: str):  # noqa: B902, N805
+        """Check that the model precision is valid."""
+        if value not in {"general", "telephonic", "meeting"}:
+            raise ValueError(f"{value} is not a valid domain type.")
+        return value
 
 
 load_dotenv()
 
 settings = Settings(
-    project_name=getenv("PROJECT_NAME"),
-    version=getenv("VERSION"),
-    description=getenv("DESCRIPTION"),
-    api_prefix=getenv("API_PREFIX"),
-    debug=getenv("DEBUG"),
-    batch_size=getenv("BATCH_SIZE"),
-    max_wait=getenv("MAX_WAIT"),
-    whisper_model=getenv("WHISPER_MODEL"),
-    embeddings_model=getenv("EMBEDDINGS_MODEL"),
-    compute_type=getenv("COMPUTE_TYPE"),
+    project_name=getenv("PROJECT_NAME", "Wordcab Transcribe"),
+    version=getenv("VERSION", "0.1.0"),
+    description=getenv(
+        "DESCRIPTION", "💬 ASR FastAPI server using faster-whisper and NVIDIA NeMo."
+    ),
+    api_prefix=getenv("API_PREFIX", "/api/v1"),
+    debug=getenv("DEBUG", True),
+    batch_size=getenv("BATCH_SIZE", 1),
+    max_wait=getenv("MAX_WAIT", 0.1),
+    whisper_model=getenv("WHISPER_MODEL", "large-v2"),
+    compute_type=getenv("COMPUTE_TYPE", "int8_float16"),
+    nemo_domain_type=getenv("NEMO_DOMAIN_TYPE", "general"),
+    nemo_storage_path=getenv("NEMO_STORAGE_PATH", "nemo_storage"),
+    nemo_output_path=getenv("NEMO_OUTPUT_PATH", "nemo_outputs"),
 )
diff --git a/wordcab_transcribe/main.py b/wordcab_transcribe/main.py
index a4fa3bb..f3f7143 100644
--- a/wordcab_transcribe/main.py
+++ b/wordcab_transcribe/main.py
@@ -29,6 +29,7 @@
 from wordcab_transcribe.service import ASRService
 from wordcab_transcribe.utils import (
     convert_file_to_wav,
+    convert_timestamp,
     delete_file,
     download_audio_file,
     download_file_from_youtube,
@@ -78,9 +79,9 @@ async def health_check():
             <h1 class="text-4xl font-medium">{settings.project_name}</h1>
             <p class="text-gray-600">Version: {settings.version}</p>
             <p class="text-gray-600">{settings.description}</p>
-            <p class="mt-16 text-gray-500">Want access? Contact us:
-                <a class="text-blue-400 text-underlined" href="mailto:info@wordcab.com?subject=Access">
-                    info@wordcab.com
+            <p class="mt-16 text-gray-500">If you find any issues, please report them to:</p>
+                <a class="text-blue-400 text-underlined" href="https://github.com/Wordcab/wordcab-transcribe/issues">
+                    wordcab/wordcab-transcribe
                 </a>
             </p>
             <a href="/docs">
@@ -123,14 +124,14 @@ async def inference_with_audio(
     else:
         data = DataRequest(**data.dict())
 
-    raw_utterances = await asr.process_input(
-        filepath, data.num_speakers, data.source_lang, data.timestamps
-    )
+    raw_utterances = await asr.process_input(filepath, data.source_lang)
+
+    timestamps_format = data.timestamps
     utterances = [
         {
             "text": format_punct(utterance["text"]),
-            "start": utterance["start"],
-            "end": utterance["end"],
+            "start": convert_timestamp(utterance["start"], timestamps_format),
+            "end": convert_timestamp(utterance["end"], timestamps_format),
             "speaker": int(utterance["speaker"]),
         }
         for utterance in raw_utterances
@@ -162,14 +163,14 @@ async def inference_with_youtube(
     else:
         data = DataRequest(**data.dict())
 
-    raw_utterances = await asr.process_input(
-        filepath, data.num_speakers, data.source_lang, data.timestamps
-    )
+    raw_utterances = await asr.process_input(filepath, data.source_lang)
+
+    timestamps_format = data.timestamps
     utterances = [
         {
             "text": format_punct(utterance["text"]),
-            "start": utterance["start"],
-            "end": utterance["end"],
+            "start": convert_timestamp(utterance["start"], timestamps_format),
+            "end": convert_timestamp(utterance["end"], timestamps_format),
             "speaker": int(utterance["speaker"]),
         }
         for utterance in raw_utterances
@@ -208,14 +209,14 @@ async def inference_with_audio_url(
     else:
         data = DataRequest(**data.dict())
 
-    raw_utterances = await asr.process_input(
-        filepath, data.num_speakers, data.source_lang, data.timestamps
-    )
+    raw_utterances = await asr.process_input(filepath, data.source_lang)
+
+    timestamps_format = data.timestamps
     utterances = [
         {
             "text": format_punct(utterance["text"]),
-            "start": utterance["start"],
-            "end": utterance["end"],
+            "start": convert_timestamp(utterance["start"], timestamps_format),
+            "end": convert_timestamp(utterance["end"], timestamps_format),
             "speaker": int(utterance["speaker"]),
         }
         for utterance in raw_utterances
diff --git a/wordcab_transcribe/models.py b/wordcab_transcribe/models.py
index 5ce6ccb..ed477e0 100644
--- a/wordcab_transcribe/models.py
+++ b/wordcab_transcribe/models.py
@@ -49,22 +49,14 @@ class Config:
 class DataRequest(BaseModel):
     """Request object for the audio file endpoint."""
 
-    num_speakers: Optional[int] = 0
     source_lang: Optional[str] = "en"
-    timestamps: Optional[str] = "seconds"
-
-    @validator("num_speakers")
-    def validate_num_speakers_values(cls, value: int) -> int:  # noqa: N805, B902
-        """Validate the value of the num_speakers field."""
-        if value < 0:
-            raise ValueError("num_speakers must be a positive integer.")
-        return value
+    timestamps: Optional[str] = "s"
 
     @validator("timestamps")
     def validate_timestamps_values(cls, value: str) -> str:  # noqa: B902, N805
         """Validate the value of the timestamps field."""
-        if value not in ["seconds", "hms"]:
-            raise ValueError("timestamps must be one of 'seconds' or 'hms'.")
+        if value not in ["hms", "ms", "s"]:
+            raise ValueError("timestamps must be one of 'hms', 'ms', 's'.")
         return value
 
     class Config:
@@ -72,8 +64,7 @@ class Config:
 
         schema_extra = {
             "example": {
-                "num_speakers": 0,
                 "source_lang": "en",
-                "timestamps": "seconds",
+                "timestamps": "s",
             }
         }
diff --git a/wordcab_transcribe/service.py b/wordcab_transcribe/service.py
index 4bf381d..bc24cc9 100644
--- a/wordcab_transcribe/service.py
+++ b/wordcab_transcribe/service.py
@@ -14,21 +14,23 @@
 """Service module to handle AI model interactions."""
 
 import asyncio
-import functools
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
 from typing import List
 
-import numpy as np
+import librosa
+import soundfile as sf
 import torch
 from faster_whisper import WhisperModel
 from loguru import logger
-from pyannote.audio import Audio
-from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
-from pyannote.core import Segment
-from sklearn.cluster import AgglomerativeClustering
-from sklearn.metrics import silhouette_score
+from nemo.collections.asr.models.msdd_models import NeuralDiarizer
 
 from wordcab_transcribe.config import settings
-from wordcab_transcribe.utils import convert_seconds_to_hms, format_segments
+from wordcab_transcribe.utils import (
+    format_segments,
+    get_segment_timestamp_anchor,
+    load_nemo_config,
+)
 
 
 class ASRService:
@@ -39,18 +41,27 @@ def __init__(self) -> None:
         self.device = "cuda" if torch.cuda.is_available() else "cpu"
         self.whisper_model = settings.whisper_model
         self.compute_type = settings.compute_type
-        self.embeddings_model = settings.embeddings_model
 
-        self.model = WhisperModel(
+        self.nemo_tmp = Path.cwd() / "temp_outputs"
+        if not self.nemo_tmp.exists():
+            self.nemo_tmp.mkdir(parents=True, exist_ok=True)
+
+        self.model_whisper = WhisperModel(
             self.whisper_model, device=self.device, compute_type=self.compute_type
         )
-        self.embedding_model = PretrainedSpeakerEmbedding(
-            self.embeddings_model, device=self.device
-        )
+        self.model_msdd = NeuralDiarizer(
+            cfg=load_nemo_config(
+                domain_type=settings.nemo_domain_type,
+                storage_path=settings.nemo_storage_path,
+                output_path=settings.nemo_output_path,
+            )
+        ).to(self.device)
+
+        self.thread_executor = ThreadPoolExecutor(max_workers=4)
 
         # Multi requests support
         self.queue = []
-        self.queue_lock = None
+        self.queue_lock = asyncio.Lock()
         self.needs_processing = None
         self.needs_processing_timer = None
 
@@ -71,39 +82,67 @@ def schedule_processing_if_needed(self) -> None:
             )
 
     async def process_input(
-        self, filepath: str, num_speakers: int, source_lang: str, timestamps: str
+        self,
+        filepath: str,
+        source_lang: str,
     ) -> List[dict]:
         """
         Process the input request and return the result.
 
         Args:
             filepath (str): Path to the audio file.
-            num_speakers (int): Number of speakers to detect.
             source_lang (str): Source language of the audio file.
-            timestamps (str): Timestamps unit to use.
 
         Returns:
             List[dict]: List of speaker segments.
         """
-        one_task = {
-            "done_event": asyncio.Event(),
+        task = {
             "input": filepath,
-            "num_speakers": num_speakers,
             "source_lang": source_lang,
-            "timestamps": timestamps,
+            "done_event": asyncio.Event(),
             "time": asyncio.get_event_loop().time(),
         }
+
         async with self.queue_lock:
-            self.queue.append(one_task)
+            self.queue.append(task)
             self.schedule_processing_if_needed()
 
-        await one_task["done_event"].wait()
+        await task["done_event"].wait()
+
+        return task["result"]
+
+    def inference_with_whisper(self, filepath: str, source_lang: str) -> List[dict]:
+        """Run inference with whisper model."""
+        segments, _ = self.model_whisper.transcribe(
+            filepath, language=source_lang, beam_size=5, word_timestamps=True
+        )
+        segments = format_segments(list(segments))
+
+        return segments
+
+    def inference_with_msdd(self, filepath: str) -> List[dict]:
+        """Run inference with msdd model."""
+        signal, sample_rate = librosa.load(filepath, sr=None)
+
+        tmp_save_path = self.nemo_tmp / "mono_file.wav"
+
+        sf.write(str(tmp_save_path), signal, sample_rate, "PCM_24")
+
+        self.model_msdd.diarize()
+
+        speaker_ts = []
+        with open(f"{settings.nemo_output_path}/pred_rttms/mono_file.rttm") as f:
+            lines = f.readlines()
+            for line in lines:
+                line_list = line.split(" ")
+                s = int(float(line_list[5]) * 1000)
+                e = s + int(float(line_list[8]) * 1000)
+                speaker_ts.append([s, e, int(line_list[11].split("_")[-1])])
 
-        return one_task["result"]
+        return speaker_ts
 
     async def runner(self) -> None:
-        """Process the input requests in the queue."""
-        self.queue_lock = asyncio.Lock()
+        """Runner method to process the queue."""
         self.needs_processing = asyncio.Event()
         while True:
             await self.needs_processing.wait()
@@ -126,24 +165,16 @@ async def runner(self) -> None:
                 self.schedule_processing_if_needed()
 
             try:
-                results = []
-                for task in file_batch:
-                    res = await asyncio.get_event_loop().run_in_executor(
-                        None,
-                        functools.partial(
-                            self.inference,
-                            task["input"],
-                            task["num_speakers"],
-                            task["source_lang"],
-                            task["timestamps"],
-                        ),
-                    )
-                    results.append(res)
-                for task, result in zip(file_batch, results):  # noqa: B905
+                results = await asyncio.get_event_loop().run_in_executor(
+                    self.thread_executor, self.process_batch, file_batch
+                )
+
+                for task, result in zip(file_batch, results):  # noqa B905
                     task["result"] = result
                     task["done_event"].set()
-                del file_batch
+
                 del results
+                del file_batch
 
             except Exception as e:
                 logger.error(f"Error processing batch: {e}")
@@ -151,202 +182,131 @@ async def runner(self) -> None:
                     task["result"] = e
                     task["done_event"].set()
 
-    def inference(
-        self,
-        filepath: str,
-        num_speakers: int,
-        source_lang: str,
-        timestamps: str,
-    ) -> List[dict]:
+    def process_batch(self, file_batch: List[dict]) -> List[dict]:
         """
-        Inference method to process the audio file.
+        Process a batch of requests.
 
         Args:
-            filepath (str): Path to the audio file.
-            num_speakers (int): Number of speakers to detect.
-            source_lang (str): Source language of the audio file.
-            timestamps (str): Timestamps unit to use.
+            file_batch (List[dict]): List of requests.
 
         Returns:
-            List[dict]: List of diarized segments.
+            List[dict]: List of results.
         """
-        segments, _ = self.model.transcribe(
-            filepath, language=source_lang, beam_size=5, word_timestamps=True
-        )
-        segments = format_segments(list(segments))
+        results = []
+        for task in file_batch:
+            filepath = task["input"]
+            source_lang = task["source_lang"]
 
-        duration = segments[-1]["end"]
-        diarized_segments = self.diarize(
-            filepath, segments, duration, num_speakers, timestamps
-        )
+            formatted_segments = self.inference_with_whisper(filepath, source_lang)
+            speaker_timestamps = self.inference_with_msdd(filepath)
 
-        return diarized_segments
-
-    def diarize(
-        self,
-        filepath: str,
-        segments: List[dict],
-        duration: float,
-        num_speakers: int,
-        timestamps: str,
-    ) -> List[dict]:
-        """
-        Diarize the segments using pyannote.
-
-        Args:
-            filepath (str): Path to the audio file.
-            segments (List[dict]): List of segments to diarize.
-            duration (float): Duration of the audio file.
-            num_speakers (int): Number of speakers; defaults to 0.
-            timestamps (str): Format of timestamps; defaults to "seconds".
-
-        Returns:
-            List[dict]: List of diarized segments with speaker labels.
-        """
-        embeddings = np.zeros(shape=(len(segments), 192))
-
-        for i, segment in enumerate(segments):
-            embeddings[i] = self.segment_embedding(filepath, segment, duration)
-
-        embeddings = np.nan_to_num(embeddings)
+            segments_with_speaker_mapping = self.segments_speaker_mapping(
+                formatted_segments, speaker_timestamps
+            )
 
-        num_speakers = num_speakers or 0
-        best_num_speakers = self._get_num_speakers(embeddings, num_speakers)
+            utterances = self.utterances_speaker_mapping(
+                segments_with_speaker_mapping, speaker_timestamps
+            )
 
-        identified_segments = self._assign_speaker_label(
-            segments, embeddings, best_num_speakers
-        )
-        joined_segments = self.join_utterances(identified_segments, timestamps)
+            results.append(utterances)
 
-        return joined_segments
+        return results
 
-    def segment_embedding(
-        self, filepath: str, segment: dict, duration: float
-    ) -> np.ndarray:
+    def segments_speaker_mapping(
+        self,
+        transcript_segments: List[dict],
+        speaker_timestamps: List[str],
+        anchor_option: str = "start",
+    ) -> List[dict]:
         """
-        Get the embedding of a segment.
+        Map each transcript segment to its corresponding speaker.
 
         Args:
-            filepath (str): Path to the audio file.
-            segment (dict): Segment to get the embedding.
-            duration (float): Duration of the audio file.
+            transcript_segments (List[dict]): List of transcript segments.
+            speaker_timestamps (List[str]): List of speaker timestamps.
+            anchor_option (str): Anchor option to use.
 
         Returns:
-            np.ndarray: Embedding of the segment.
+            List[dict]: List of transcript segments with speaker mapping.
         """
-        start = segment["start"]
-        end = min(duration, segment["end"])
-
-        clip = Segment(start=start, end=end)
-
-        audio = Audio()
-        waveform, _ = audio.crop(filepath, clip)
-
-        return self.embedding_model(waveform[None])
-
-    def join_utterances(self, segments: List[dict], timestamps: str) -> List[dict]:
-        """
-        Join the segments of the same speaker.
+        _, end, speaker = speaker_timestamps[0]
+        segment_position, turn_idx = 0, 0
+        segment_speaker_mapping = []
+
+        for segment in transcript_segments:
+            segment_start, segment_end, segment_text = (
+                int(segment["start"] * 1000),
+                int(segment["end"] * 1000),
+                segment["text"],
+            )
 
-        Args:
-            segments (List[dict]): List of segments.
-            timestamps (str): Format of timestamps to use.
+            segment_position = get_segment_timestamp_anchor(
+                segment_start, segment_end, anchor_option
+            )
 
-        Returns:
-            List[dict]: List of joined segments with speaker labels.
-        """
-        utterance_list = []
-        current_utterance = None
-        text = ""
-
-        for idx, segment in enumerate(segments):
-            if idx == 0 or segments[idx - 1]["speaker"] != segment["speaker"]:
-                if current_utterance is not None:
-                    current_utterance["end"] = segments[idx - 1]["end"]
-                    current_utterance["text"] = text.strip()
-                    utterance_list.append(current_utterance)
-                    text = ""
-
-                current_utterance = {
-                    "start": segment["start"],
-                    "speaker": segment["speaker"],
+            while segment_position > float(end):
+                turn_idx += 1
+                turn_idx = min(turn_idx, len(speaker_timestamps) - 1)
+                _, end, speaker = speaker_timestamps[turn_idx]
+                if turn_idx == len(speaker_timestamps) - 1:
+                    end = get_segment_timestamp_anchor(
+                        segment_start, segment_end, option="end"
+                    )
+                    break
+
+            segment_speaker_mapping.append(
+                {
+                    "start": segment_start,
+                    "end": segment_end,
+                    "text": segment_text,
+                    "speaker": speaker,
                 }
+            )
 
-            text += segment["text"] + " "
-
-        if current_utterance:
-            current_utterance["end"] = segments[idx]["end"]
-            current_utterance["text"] = text.strip()
-            utterance_list.append(current_utterance)
-
-        for utterance in utterance_list:
-            if timestamps == "hms":
-                utterance["start"] = convert_seconds_to_hms(utterance["start"])
-                utterance["end"] = convert_seconds_to_hms(utterance["end"])
-            elif timestamps == "seconds":
-                utterance["start"] = float(utterance["start"])
-                utterance["end"] = float(utterance["end"])
-            elif timestamps == "milliseconds":
-                utterance["start"] = float(utterance["start"] * 1000)
-                utterance["end"] = float(utterance["end"] * 1000)
-
-        return utterance_list
+        return segment_speaker_mapping
 
-    def _get_num_speakers(self, embeddings: np.ndarray, num_speakers: int) -> int:
+    def utterances_speaker_mapping(
+        self, transcript_segments: List[dict], speaker_timestamps: List[dict]
+    ) -> List[dict]:
         """
-        Get the number of speakers in the audio file.
+        Map utterances of the same speaker together.
 
         Args:
-            embeddings (np.ndarray): Embeddings of the segments.
-            num_speakers (int): Number of speakers.
+            transcript_segments (List[dict]): List of transcript segments.
+            speaker_timestamps (List[dict]): List of speaker timestamps.
 
         Returns:
-            int: Number of speakers.
+            List[dict]: List of sentences with speaker mapping.
         """
-        if num_speakers == 0:
-            score_num_speakers = {}
-            try:
-                for i in range(2, 11):
-                    clustering = AgglomerativeClustering(i).fit(embeddings)
-                    score = silhouette_score(
-                        embeddings, clustering.labels_, metric="euclidean"
-                    )
-                    score_num_speakers[i] = score
-
-                best_num_speakers = max(
-                    score_num_speakers, key=lambda x: score_num_speakers[x]
-                )
-            except Exception as e:
-                logger.warning(
-                    f"Error while getting number of speakers: {e}, defaulting to 1"
-                )
-                best_num_speakers = 1
-        else:
-            best_num_speakers = num_speakers
-
-        return best_num_speakers
+        start_t0, end_t0, speaker_t0 = speaker_timestamps[0]
+        previous_speaker = speaker_t0
+
+        sentences = []
+        current_sentence = {
+            "speaker": speaker_t0,
+            "start": start_t0,
+            "end": end_t0,
+            "text": "",
+        }
 
-    def _assign_speaker_label(
-        self, segments: List[dict], embeddings: np.ndarray, best_num_speakers: int
-    ) -> List[int]:
-        """
-        Assign a speaker label to each segment.
+        for segment in transcript_segments:
+            text_segment, speaker = segment["text"], segment["speaker"]
+            start_t, end_t = segment["start"], segment["end"]
+
+            if speaker != previous_speaker:
+                sentences.append(current_sentence)
+                current_sentence = {
+                    "speaker": speaker,
+                    "start": start_t,
+                    "end": end_t,
+                    "text": "",
+                }
+            else:
+                current_sentence["end"] = end_t
 
-        Args:
-            segments (List[dict]): List of segments.
-            embeddings (np.ndarray): Embeddings of the segments.
-            best_num_speakers (int): Number of speakers.
+            current_sentence["text"] += text_segment + " "
+            previous_speaker = speaker
 
-        Returns:
-            List[int]: List of segments with speaker labels.
-        """
-        if best_num_speakers == 1:
-            for i in range(len(segments)):
-                segments[i]["speaker"] = 1
-        else:
-            clustering = AgglomerativeClustering(best_num_speakers).fit(embeddings)
-            labels = clustering.labels_
-            for i in range(len(segments)):
-                segments[i]["speaker"] = labels[i] + 1
+        sentences.append(current_sentence)
 
-        return segments
+        return sentences
diff --git a/wordcab_transcribe/utils.py b/wordcab_transcribe/utils.py
index 866095c..bbed8c7 100644
--- a/wordcab_transcribe/utils.py
+++ b/wordcab_transcribe/utils.py
@@ -13,17 +13,19 @@
 # limitations under the License.
 """Utils module of the Wordcab Transcribe."""
 import asyncio
+import json
 import math
 import mimetypes
 import re
 import subprocess  # noqa: S404
 import sys
 from pathlib import Path
-from typing import Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
 import aiofiles
 import aiohttp
 from loguru import logger
+from omegaconf import OmegaConf
 from yt_dlp import YoutubeDL
 
 
@@ -45,17 +47,43 @@ async def run_subprocess(command: List[str]) -> tuple:
     return process.returncode, stdout, stderr
 
 
-def convert_seconds_to_hms(seconds: float) -> str:
+def convert_timestamp(timestamp: float, target: str) -> Union[str, float]:
     """
-    Convert seconds to hours, minutes and seconds.
+    Use the right function to convert the timestamp.
 
     Args:
-        seconds (float): Seconds to convert.
+        timestamp (float): Timestamp to convert.
+        target (str): Timestamp to convert.
+
+    Returns:
+        Union[str, float]: Converted timestamp.
+
+    Raises:
+        ValueError: If the target is invalid. Valid targets are: ms, hms, s.
+    """
+    if target == "ms":
+        return timestamp
+    elif target == "hms":
+        return _convert_ms_to_hms(timestamp)
+    elif target == "s":
+        return _convert_ms_to_s(timestamp)
+    else:
+        raise ValueError(
+            f"Invalid conversion target: {target}. Valid targets are: ms, hms, s."
+        )
+
+
+def _convert_ms_to_hms(timestamp: float) -> str:
+    """
+    Convert a timestamp from milliseconds to hours, minutes and seconds.
+
+    Args:
+        timestamp (float): Timestamp in milliseconds to convert.
 
     Returns:
         str: Hours, minutes and seconds.
     """
-    hours, remainder = divmod(seconds, 3600)
+    hours, remainder = divmod(timestamp / 1000, 3600)
     minutes, seconds = divmod(remainder, 60)
     milliseconds = math.floor((seconds % 1) * 1000)
 
@@ -64,6 +92,19 @@ def convert_seconds_to_hms(seconds: float) -> str:
     return output
 
 
+def _convert_ms_to_s(timestamp: float) -> float:
+    """
+    Convert a timestamp from milliseconds to seconds.
+
+    Args:
+        timestamp (float): Timestamp in milliseconds to convert.
+
+    Returns:
+        float: Seconds.
+    """
+    return timestamp / 1000
+
+
 async def convert_file_to_wav(filepath: str) -> str:
     """
     Convert a file to wav format using ffmpeg.
@@ -229,7 +270,7 @@ def format_segments(
     segments: list,
     use_dict: Optional[bool] = False,
     include_words: Optional[bool] = False,
-) -> list:
+) -> List[dict]:
     """
     Format the segments to a list of dicts with start, end and text keys.
 
@@ -273,6 +314,68 @@ def format_segments(
     return formatted_segments
 
 
+def get_segment_timestamp_anchor(start: float, end: float, option: str = "start"):
+    """Get the timestamp anchor for a segment."""
+    if option == "end":
+        return end
+    elif option == "mid":
+        return (start + end) / 2
+    return start
+
+
+def load_nemo_config(
+    domain_type: str, storage_path: str, output_path: str
+) -> Dict[str, Any]:
+    """
+    Load NeMo config file based on a domain type.
+
+    Args:
+        domain_type (str): The domain type. Can be "general", "meeting" or "telephonic".
+        storage_path (str): The path to the NeMo storage directory.
+        output_path (str): The path to the NeMo output directory.
+
+    Returns:
+        Dict[str, Any]: The config file as a dict.
+    """
+    cfg_path = (
+        Path(__file__).parent.parent
+        / "config"
+        / "nemo"
+        / f"diar_infer_{domain_type}.yaml"
+    )
+    with open(cfg_path) as f:
+        cfg = OmegaConf.load(f)
+
+    storage_path = Path(__file__).parent.parent / storage_path
+    if not storage_path.exists():
+        storage_path.mkdir(parents=True, exist_ok=True)
+
+    meta = {
+        "audio_filepath": "/app/temp_outputs/mono_file.wav",
+        "offset": 0,
+        "duration": None,
+        "label": "infer",
+        "text": "-",
+        "rttm_filepath": None,
+        "uem_filepath": None,
+    }
+
+    manifest_path = storage_path / "infer_manifest.json"
+    with open(manifest_path, "w") as fp:
+        json.dump(meta, fp)
+        fp.write("\n")
+
+    output_path = Path(__file__).parent.parent / output_path
+    if not output_path.exists():
+        output_path.mkdir(parents=True, exist_ok=True)
+
+    cfg.num_workers = 0
+    cfg.diarizer.manifest_filepath = str(manifest_path)
+    cfg.diarizer.out_dir = str(output_path)
+
+    return cfg
+
+
 def retrieve_user_platform() -> str:
     """
     Retrieve the user's platform.