Increasing timeout for remote diarization auth

.env

@@ -37,10 +37,10 @@ DEBUG=True
 # Then in your Dockerfile, copy the converted models to the /app/src/wordcab_transcribe/whisper_models folder.
 # Example for WHISPER_MODEL: COPY cloned_wordcab_transcribe_repo/src/wordcab_transcribe/whisper_models/large-v3 /app/src/wordcab_transcribe/whisper_models/large-v3
 # Example for ALIGN_MODEL: COPY cloned_wordcab_transcribe_repo/src/wordcab_transcribe/whisper_models/tiny /app/src/wordcab_transcribe/whisper_models/tiny
-WHISPER_MODEL="large-v3"
+WHISPER_MODEL="medium"
 # You can specify one of two engines, "faster-whisper" or "tensorrt-llm". At the moment, "faster-whisper" is more
 # stable, adjustable, and accurate, while "tensorrt-llm" is faster but less accurate and adjustable.
-WHISPER_ENGINE="tensorrt-llm"
+WHISPER_ENGINE="faster-whisper-batched"
 # This helps adjust some build during the conversion of the Whisper model to TensorRT. If you change this, be sure to
 # it in pre_requirements.txt. The only available options are "0.9.0.dev2024032600" and "0.11.0.dev2024052100".
 # Note that version "0.11.0.dev2024052100" is not compatible with T4 or V100 GPUs.

Dockerfile

@@ -62,6 +62,8 @@ RUN curl -L ${RELEASE_URL} | tar -zx -C /tmp \
 
 RUN python -m pip install pip --upgrade
 
+COPY faster-whisper /app/faster-whisper
+
 COPY pre_requirements.txt .
 COPY requirements.txt .
 

pre_requirements.txt

@@ -10,4 +10,5 @@ tensorrt_llm==0.9.0.dev2024032600
 Cython==3.0.10
 youtokentome @ git+https://github.com/gburlet/YouTokenToMe.git@dependencies
 deepmultilingualpunctuation==1.0.1
-pyannote.audio==3.2.0
+pyannote.audio==3.2.0
+ipython==8.24.0

requirements.txt

@@ -1,7 +1,7 @@
 aiohttp==3.9.3
 aiofiles==23.2.1
 boto3
-faster-whisper @ https://github.com/SYSTRAN/faster-whisper/archive/refs/heads/master.tar.gz
+-e /app/faster-whisper
 ffmpeg-python==0.2.0
 transformers==4.38.2
 librosa==0.10.1

src/wordcab_transcribe/config.py

@@ -126,9 +126,9 @@ def whisper_model_compatibility_check(cls, value: str):  # noqa: B902, N805
     @field_validator("whisper_engine")
     def whisper_engine_compatibility_check(cls, value: str):  # noqa: B902, N805
         """Check that the whisper engine is compatible."""
-        if value.lower() not in ["faster-whisper", "tensorrt-llm"]:
+        if value.lower() not in ["faster-whisper", "faster-whisper-batched", "tensorrt-llm"]:
             raise ValueError(
-                "The whisper engine must be one of `faster-whisper` or `tensorrt-llm`."
+                "The whisper engine must be one of `faster-whisper`, `faster-whisper-batched`, or `tensorrt-llm`."
             )
 
         return value

src/wordcab_transcribe/router/v1/audio_file_endpoint.py

@@ -117,7 +117,6 @@ async def inference_with_audio(  # noqa: C901
     )
 
     background_tasks.add_task(delete_file, filepath=filename)
-
     task = asyncio.create_task(
         asr.process_input(
             filepath=filepath,

src/wordcab_transcribe/services/asr_service.py

@@ -439,7 +439,7 @@ async def process_input(  # noqa: C901
             filepath (Union[str, List[str]]):
                 Path to the audio file or list of paths to the audio files to process.
             batch_size (Union[int, None]):
-                The batch size to use for the transcription. For tensorrt-llm whisper engine only.
+                The batch size to use for the transcription. For tensorrt-llm and faster-whisper-batch engines only.
             offset_start (Union[float, None]):
                 The start time of the audio file to process.
             offset_end (Union[float, None]):
@@ -611,11 +611,20 @@ async def process_transcription(self, task: ASRTask, debug_mode: bool) -> None:
             if isinstance(task.transcription.execution, LocalExecution):
                 out = await time_and_tell_async(
                     lambda: self.local_services.transcription(
-                        task.audio,
+                        audio=task.audio,
                         model_index=task.transcription.execution.index,
-                        suppress_blank=False,
-                        word_timestamps=True,
-                        **task.transcription.options.model_dump(),
+                        source_lang=task.transcription.options.source_lang,
+                        batch_size=task.batch_size,
+                        num_beams=task.transcription.options.num_beams,
+                        suppress_blank=False,  # TODO: Add this to the options
+                        vocab=task.transcription.options.vocab,
+                        word_timestamps=task.word_timestamps,
+                        internal_vad=task.transcription.options.internal_vad,
+                        repetition_penalty=task.transcription.options.repetition_penalty,
+                        compression_ratio_threshold=task.transcription.options.compression_ratio_threshold,
+                        log_prob_threshold=task.transcription.options.log_prob_threshold,
+                        no_speech_threshold=task.transcription.options.no_speech_threshold,
+                        condition_on_previous_text=task.transcription.options.condition_on_previous_text,
                     ),
                     func_name="transcription",
                     debug_mode=debug_mode,
@@ -880,7 +889,8 @@ async def remote_diarization(
         if not settings.debug:
             headers = {"Content-Type": "application/x-www-form-urlencoded"}
             auth_url = f"{url}/api/v1/auth"
-            async with aiohttp.ClientSession() as session:
+            diarization_timeout = aiohttp.ClientTimeout(total=10)
+            async with AsyncLocationTrustedRedirectSession(timeout=diarization_timeout) as session:
                 async with session.post(
                     url=auth_url,
                     data={"username": settings.username, "password": settings.password},

src/wordcab_transcribe/services/transcribe_service.py

@@ -21,9 +21,9 @@
 from typing import Iterable, List, NamedTuple, Optional, Union
 
 import torch
-from faster_whisper import WhisperModel
 from loguru import logger
 from tensorshare import Backend, TensorShare
+from faster_whisper import WhisperModel, BatchedInferencePipeline
 
 from wordcab_transcribe.config import settings
 from wordcab_transcribe.engines.tensorrt_llm.model import WhisperModelTRT
@@ -87,6 +87,16 @@ def __init__(
                 device_index=device_index,
                 compute_type=self.compute_type,
             )
+        elif self.model_engine == "faster-whisper-batched":
+            logger.info("Using faster-whisper-batched model engine.")
+            self.model = BatchedInferencePipeline(
+                model=WhisperModel(
+                    self.model_path,
+                    device=self.device,
+                    device_index=device_index,
+                    compute_type=self.compute_type,
+                )
+            )
         elif self.model_engine == "tensorrt-llm":
             logger.info("Using tensorrt-llm model engine.")
             if "v3" in self.model_path:
@@ -126,7 +136,7 @@ def __call__(
         ],
         source_lang: str,
         model_index: int,
-        batch_size: int = 1,
+        batch_size: int,
         num_beams: int = 1,
         suppress_blank: bool = False,
         vocab: Union[List[str], None] = None,
@@ -220,6 +230,21 @@ def __call__(
                         "window_size_samples": 512,
                     },
                 )
+            elif self.model_engine == "faster-whisper-batched":
+                print("Batch size: ", batch_size)
+                segments, _ = self.model.transcribe(
+                    audio,
+                    language=source_lang,
+                    hotwords=prompt,
+                    beam_size=num_beams,
+                    repetition_penalty=repetition_penalty,
+                    compression_ratio_threshold=compression_ratio_threshold,
+                    log_prob_threshold=log_prob_threshold,
+                    no_speech_threshold=no_speech_threshold,
+                    suppress_blank=suppress_blank,
+                    word_timestamps=word_timestamps,
+                    batch_size=batch_size,
+                )
             elif self.model_engine == "tensorrt-llm":
                 segments = self.model.transcribe(
                     audio_data=[audio],