minor fix

README.md

@@ -43,14 +43,10 @@ pip install git+https://github.com/SWivid/F5-TTS.git
 ```bash
 git clone https://github.com/SWivid/F5-TTS.git
 cd F5-TTS
+# git submodule update --init --recursive  # (optional, if need bigvgan)
 pip install -e .
-
-# Init submodule (optional, if you want to change the vocoder from vocos to bigvgan)
-# git submodule update --init --recursive
-# pip install -e .
 ```
-
-After init submodule, you need to change the `src/third_party/BigVGAN/bigvgan.py` by adding the following code at the beginning of the file.
+If initialize submodule, you should add the following code at the beginning of `src/third_party/BigVGAN/bigvgan.py`.
 ```python
 import os
 import sys

src/f5_tts/eval/eval_infer_batch.py

@@ -120,6 +120,7 @@ def main():
         target_sample_rate=target_sample_rate,
         n_mel_channels=n_mel_channels,
         hop_length=hop_length,
+        mel_spec_type=mel_spec_type,
         target_rms=target_rms,
         use_truth_duration=use_truth_duration,
         infer_batch_size=infer_batch_size,
@@ -153,12 +154,7 @@ def main():
         vocab_char_map=vocab_char_map,
     ).to(device)
 
-    supports_fp16 = device == "cuda" and torch.cuda.get_device_properties(device).major >= 6
-    if supports_fp16 and mel_spec_type == "vocos":
-        dtype = torch.float16
-    elif mel_spec_type == "bigvgan":
-        dtype = torch.float32
-
+    dtype = torch.float32 if mel_spec_type == "bigvgan" else None
     model = load_checkpoint(model, ckpt_path, device, dtype=dtype, use_ema=use_ema)
 
     if not os.path.exists(output_dir) and accelerator.is_main_process:

src/f5_tts/eval/utils_eval.py

@@ -78,7 +78,7 @@ def get_inference_prompt(
     win_length=1024,
     n_mel_channels=100,
     hop_length=256,
-    mel_spec_type="bigvgan",
+    mel_spec_type="vocos",
     target_rms=0.1,
     use_truth_duration=False,
     infer_batch_size=1,

src/f5_tts/infer/README.md

@@ -58,8 +58,8 @@ f5-tts_infer-cli \
 --gen_text "Some text you want TTS model generate for you."
 
 # Choose Vocoder
-f5-tts_infer-cli --vocoder_name bigvgan --load_vocoder_from_local --ckpt_file <YOUR_CKPT_PATH, eg:ckpts/model_1250000.pt >
-f5-tts_infer-cli --vocoder_name vocos --load_vocoder_from_local --ckpt_file <YOUR_CKPT_PATH, eg:ckpts/F5TTS_Base/model_1200000.safetensors  >
+f5-tts_infer-cli --vocoder_name bigvgan --load_vocoder_from_local --ckpt_file <YOUR_CKPT_PATH, eg:ckpts/F5TTS_Base_bigvgan/model_1250000.pt>
+f5-tts_infer-cli --vocoder_name vocos --load_vocoder_from_local --ckpt_file <YOUR_CKPT_PATH, eg:ckpts/F5TTS_Base/model_1200000.safetensors>
 ```
 
 And a `.toml` file would help with more flexible usage.

src/f5_tts/infer/speech_edit.py

@@ -111,12 +111,7 @@
     vocab_char_map=vocab_char_map,
 ).to(device)
 
-supports_fp16 = device == "cuda" and torch.cuda.get_device_properties(device).major >= 6
-if supports_fp16 and mel_spec_type == "vocos":
-    dtype = torch.float16
-elif mel_spec_type == "bigvgan":
-    dtype = torch.float32
-
+dtype = torch.float32 if mel_spec_type == "bigvgan" else None
 model = load_checkpoint(model, ckpt_path, device, dtype=dtype, use_ema=use_ema)
 
 # Audio

src/f5_tts/infer/utils_infer.py

@@ -40,6 +40,7 @@
 hop_length = 256
 win_length = 1024
 n_fft = 1024
+mel_spec_type = "vocos"
 target_rms = 0.1
 cross_fade_duration = 0.15
 ode_method = "euler"
@@ -131,7 +132,7 @@ def initialize_asr_pipeline(device=device):
 # load model checkpoint for inference
 
 
-def load_checkpoint(model, ckpt_path, device, dtype, use_ema=True):
+def load_checkpoint(model, ckpt_path, device, dtype=None, use_ema=True):
     if dtype is None:
         dtype = (
             torch.float16 if device == "cuda" and torch.cuda.get_device_properties(device).major >= 6 else torch.float32
@@ -175,7 +176,7 @@ def load_model(
     model_cls,
     model_cfg,
     ckpt_path,
-    mel_spec_type="vocos",
+    mel_spec_type=mel_spec_type,
     vocab_file="",
     ode_method=ode_method,
     use_ema=True,
@@ -206,12 +207,7 @@ def load_model(
         vocab_char_map=vocab_char_map,
     ).to(device)
 
-    supports_fp16 = device == "cuda" and torch.cuda.get_device_properties(device).major >= 6
-    if supports_fp16 and mel_spec_type == "vocos":
-        dtype = torch.float16
-    elif mel_spec_type == "bigvgan":
-        dtype = torch.float32
-
+    dtype = torch.float32 if mel_spec_type == "bigvgan" else None
     model = load_checkpoint(model, ckpt_path, device, dtype=dtype, use_ema=use_ema)
 
     return model
@@ -307,7 +303,7 @@ def infer_process(
     gen_text,
     model_obj,
     vocoder,
-    mel_spec_type="vocos",
+    mel_spec_type=mel_spec_type,
     show_info=print,
     progress=tqdm,
     target_rms=target_rms,

src/f5_tts/model/modules.py

@@ -19,57 +19,44 @@
 from torch import nn
 from x_transformers.x_transformers import apply_rotary_pos_emb
 
-# raw wav to mel spec
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
 
-def dynamic_range_decompression_torch(x, C=1):
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    return dynamic_range_compression_torch(magnitudes)
+# raw wav to mel spec
 
 
 mel_basis_cache = {}
 hann_window_cache = {}
 
 
-# BigVGAN extract mel spectrogram
-def mel_spectrogram(
-    y: torch.Tensor,
-    n_fft: int,
-    num_mels: int,
-    sampling_rate: int,
-    hop_size: int,
-    win_size: int,
-    fmin: int,
-    fmax: int = None,
-    center: bool = False,
-) -> torch.Tensor:
-    """Copy from https://github.com/NVIDIA/BigVGAN/tree/main"""
-    device = y.device
-    key = f"{n_fft}_{num_mels}_{sampling_rate}_{hop_size}_{win_size}_{fmin}_{fmax}_{device}"
+def get_bigvgan_mel_spectrogram(
+    waveform,
+    n_fft=1024,
+    n_mel_channels=100,
+    target_sample_rate=24000,
+    hop_length=256,
+    win_length=1024,
+    fmin=0,
+    fmax=None,
+    center=False,
+):  # Copy from https://github.com/NVIDIA/BigVGAN/tree/main
+    device = waveform.device
+    key = f"{n_fft}_{n_mel_channels}_{target_sample_rate}_{hop_length}_{win_length}_{fmin}_{fmax}_{device}"
 
     if key not in mel_basis_cache:
-        mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
+        mel = librosa_mel_fn(sr=target_sample_rate, n_fft=n_fft, n_mels=n_mel_channels, fmin=fmin, fmax=fmax)
         mel_basis_cache[key] = torch.from_numpy(mel).float().to(device)  # TODO: why they need .float()?
-        hann_window_cache[key] = torch.hann_window(win_size).to(device)
+        hann_window_cache[key] = torch.hann_window(win_length).to(device)
 
     mel_basis = mel_basis_cache[key]
     hann_window = hann_window_cache[key]
 
-    padding = (n_fft - hop_size) // 2
-    y = torch.nn.functional.pad(y.unsqueeze(1), (padding, padding), mode="reflect").squeeze(1)
+    padding = (n_fft - hop_length) // 2
+    waveform = torch.nn.functional.pad(waveform.unsqueeze(1), (padding, padding), mode="reflect").squeeze(1)
 
     spec = torch.stft(
-        y,
+        waveform,
         n_fft,
-        hop_length=hop_size,
-        win_length=win_size,
+        hop_length=hop_length,
+        win_length=win_length,
         window=hann_window,
         center=center,
         pad_mode="reflect",
@@ -80,31 +67,11 @@ def mel_spectrogram(
     spec = torch.sqrt(torch.view_as_real(spec).pow(2).sum(-1) + 1e-9)
 
     mel_spec = torch.matmul(mel_basis, spec)
-    mel_spec = spectral_normalize_torch(mel_spec)
+    mel_spec = torch.log(torch.clamp(mel_spec, min=1e-5))
 
     return mel_spec
 
 
-def get_bigvgan_mel_spectrogram(
-    waveform,
-    n_fft=1024,
-    n_mel_channels=100,
-    target_sample_rate=24000,
-    hop_length=256,
-    win_length=1024,
-):
-    return mel_spectrogram(
-        waveform,
-        n_fft,  # 1024
-        n_mel_channels,  # 100
-        target_sample_rate,  # 24000
-        hop_length,  # 256
-        win_length,  # 1024
-        fmin=0,  # 0
-        fmax=None,  # null
-    )
-
-
 def get_vocos_mel_spectrogram(
     waveform,
     n_fft=1024,

src/f5_tts/train/train.py

@@ -13,7 +13,7 @@
 hop_length = 256
 win_length = 1024
 n_fft = 1024
-mel_spec_type = "bigvgan"  # 'vocos' or 'bigvgan'
+mel_spec_type = "vocos"  # 'vocos' or 'bigvgan'
 
 tokenizer = "pinyin"  # 'pinyin', 'char', or 'custom'
 tokenizer_path = None  # if tokenizer = 'custom', define the path to the tokenizer you want to use (should be vocab.txt)