gitea-mirror/F5-TTS
1
0
Fork 0
mirror of https://github.com/SWivid/F5-TTS.git synced 2025-12-05 20:40:12 -08:00
Code Issues Packages Projects Releases Wiki Activity

update slicer in finetune_gradio, legacy min_length 2s changed to 20s

Browse Source
This commit is contained in:
SWivid
2025-07-08 16:59:46 +08:00
parent 69b0e0110e
commit 9842314127
1 changed files with 3 additions and 37 deletions
Download Patch File Download Diff File Expand all files Collapse all files

40
src/f5_tts/train/finetune_gradio.py
View File

@@ -178,45 +178,12 @@ def get_audio_duration(audio_path):
return audio.shape[1] / sample_rate
def get_rms(
y,
frame_length=2048,
hop_length=512,
pad_mode="constant",
): # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py
padding = (int(frame_length // 2), int(frame_length // 2))
y = np.pad(y, padding, mode=pad_mode)
axis = -1
# put our new within-frame axis at the end for now
out_strides = y.strides + tuple([y.strides[axis]])
# Reduce the shape on the framing axis
x_shape_trimmed = list(y.shape)
x_shape_trimmed[axis] -= frame_length - 1
out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides)
if axis < 0:
target_axis = axis - 1
else:
target_axis = axis + 1
xw = np.moveaxis(xw, -1, target_axis)
# Downsample along the target axis
slices = [slice(None)] * xw.ndim
slices[axis] = slice(0, None, hop_length)
x = xw[tuple(slices)]
# Calculate power
power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
return np.sqrt(power)
class Slicer: # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py
def __init__(
self,
sr: int,
threshold: float = -40.0,
min_length: int = 2000,
min_length: int = 20000, # 20 seconds
min_interval: int = 300,
hop_size: int = 20,
max_sil_kept: int = 2000,
@@ -247,7 +214,7 @@ class Slicer: # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.
samples = waveform
if samples.shape[0] <= self.min_length:
return [waveform]
rms_list = get_rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
sil_tags = []
silence_start = None
clip_start = 0
@@ -301,8 +268,7 @@ class Slicer: # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.
silence_end = min(total_frames, silence_start + self.max_sil_kept)
pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start
sil_tags.append((pos, total_frames + 1))
# Apply and return slices.
####音频+起始时间+终止时间
# Apply and return slices: [chunk, start, end]
if len(sil_tags) == 0:
return [[waveform, 0, int(total_frames * self.hop_size)]]
else: