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Merge branch 'dev' of github.com:litagin02/Style-Bert-VITS2

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tsukumi
2025-03-26 20:09:40 +09:00
parent 2aa503233a 3383458317
commit 1a44c9d437
15 changed files with 37 additions and 114 deletions
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13
gradio_tabs/dataset.py
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@@ -177,9 +177,16 @@ def create_dataset_app() -> gr.Blocks:
value=False,
)
hf_repo_id = gr.Dropdown(
["openai/whisper", "kotoba-tech/kotoba-whisper-v1.1"],
label="HuggingFaceのWhisperモデル",
value="openai/whisper",
[
"openai/whisper-large-v3-turbo",
"openai/whisper-large-v3",
"openai/whisper-large-v2",
"kotoba-tech/kotoba-whisper-v1.1",
"kotoba-tech/kotoba-whisper-v2.1",
"litagin/anime-whisper",
],
label="HuggingFaceのWhisper repo_id",
value="openai/whisper-large-v3",
visible=False,
)
compute_type = gr.Dropdown(

2
gradio_tabs/merge.py
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@@ -897,7 +897,7 @@ initial_md = """
- 話者数が違うモデル同士はおそらくマージできません。
"""
style_merge_md = f"""
style_merge_md = """
## 3. スタイルベクトルのマージ
1. マージ後のモデルにいくつスタイルを追加したいかを「作りたいスタイル数」で指定

2
preprocess_text.py
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@@ -2,7 +2,7 @@ import argparse
import json
from collections import defaultdict
from pathlib import Path
from random import sample, shuffle
from random import sample
from typing import Optional
from tqdm import tqdm

3
style_bert_vits2/models/infer_onnx.py
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@@ -1,4 +1,5 @@
from typing import Any, Optional, Sequence, Union
from collections.abc import Sequence
from typing import Any, Optional, Union
import numpy as np
import onnxruntime

6
style_bert_vits2/models/utils/__init__.py
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@@ -11,8 +11,10 @@ import torch
from numpy.typing import NDArray
from style_bert_vits2.logging import logger
from style_bert_vits2.models.utils import checkpoints # type: ignore
from style_bert_vits2.models.utils import safetensors # type: ignore
from style_bert_vits2.models.utils import (
checkpoints, # type: ignore # noqa: F401
safetensors, # type: ignore # noqa: F401
)
if TYPE_CHECKING:

3
style_bert_vits2/nlp/__init__.py
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@@ -1,6 +1,7 @@
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional, Sequence, Union
from collections.abc import Sequence
from typing import TYPE_CHECKING, Any, Optional, Union
from numpy.typing import NDArray

3
style_bert_vits2/nlp/chinese/bert_feature.py
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@@ -1,6 +1,7 @@
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional, Sequence, Union
from collections.abc import Sequence
from typing import TYPE_CHECKING, Any, Optional, Union
import numpy as np
import onnxruntime

3
style_bert_vits2/nlp/english/bert_feature.py
View File

@@ -1,6 +1,7 @@
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional, Sequence, Union
from collections.abc import Sequence
from typing import TYPE_CHECKING, Any, Optional, Union
import numpy as np
import onnxruntime

3
style_bert_vits2/nlp/japanese/bert_feature.py
View File

@@ -1,6 +1,7 @@
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional, Sequence, Union
from collections.abc import Sequence
from typing import TYPE_CHECKING, Any, Optional, Union
import numpy as np
import onnxruntime

3
style_bert_vits2/nlp/onnx_bert_models.py
View File

@@ -11,8 +11,9 @@ Style-Bert-VITS2 の ONNX 推論に必要な各言語ごとの ONNX 版 BERT モ
import gc
import time
from collections.abc import Sequence
from pathlib import Path
from typing import Any, Optional, Sequence, Union
from typing import Any, Optional, Union
import onnxruntime
from huggingface_hub import hf_hub_download

3
style_bert_vits2/tts_model.py
View File

@@ -2,8 +2,9 @@ from __future__ import annotations
import gc
import time
from collections.abc import Sequence
from pathlib import Path
from typing import TYPE_CHECKING, Any, Optional, Sequence, Union
from typing import TYPE_CHECKING, Any, Optional, Union
import numpy as np
import onnxruntime

3
style_bert_vits2/utils/__init__.py
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@@ -1,4 +1,5 @@
from typing import Any, Sequence, Union
from collections.abc import Sequence
from typing import Any, Union
import onnxruntime

3
tests/test_main.py
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@@ -1,4 +1,5 @@
from typing import Any, Literal, Sequence
from collections.abc import Sequence
from typing import Any, Literal
import pytest
from scipy.io import wavfile

7
transcribe.py
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@@ -67,8 +67,7 @@ def transcribe_files_with_hf_whisper(
"num_beams": num_beams,
"no_repeat_ngram_size": no_repeat_ngram_size,
}
logger.info(f"generate_kwargs: {generate_kwargs}")
logger.info(f"generate_kwargs: {generate_kwargs}, loading pipeline...")
pipe = pipeline(
model=model_id,
max_new_tokens=128,
@@ -79,6 +78,7 @@ def transcribe_files_with_hf_whisper(
trust_remote_code=True,
# generate_kwargs=generate_kwargs,
)
logger.info("Loaded pipeline")
if initial_prompt is not None:
prompt_ids: torch.Tensor = pipe.tokenizer.get_prompt_ids(
initial_prompt, return_tensors="pt"
@@ -195,9 +195,6 @@ if __name__ == "__main__":
wav_rel_path = wav_file.relative_to(input_dir)
with open(output_file, "a", encoding="utf-8") as f:
f.write(f"{wav_rel_path}|{model_name}|{language_id}|{text}\n")
else:
if args.hf_repo_id == "":
model_id = f"openai/whisper-{args.model}"
else:
model_id = args.hf_repo_id
logger.info(f"Loading HF Whisper model ({model_id})")

92
vad_filter.py
View File

@@ -1,92 +0,0 @@
import argparse
import os
import shutil
import sys
from pathlib import Path
import pandas as pd
import torch
from tqdm import tqdm
from style_bert_vits2.logging import logger
vad_model, utils = torch.hub.load(
repo_or_dir="litagin02/silero-vad",
model="silero_vad",
onnx=True,
trust_repo=True,
)
(get_speech_timestamps, _, read_audio, *_) = utils
def get_speech_ratio(audio_file):
sampling_rate = 16000
wav = read_audio(audio_file, sampling_rate=sampling_rate)
speech_timestamps = get_speech_timestamps(
wav, vad_model, sampling_rate=sampling_rate
)
speech_dur_ms = 0
for ts in speech_timestamps:
start_ms = ts["start"] / 16
end_ms = ts["end"] / 16
speech_dur_ms += end_ms - start_ms
total_dur_ms = len(wav) / sampling_rate * 1000
return speech_dur_ms / total_dur_ms
def process(file: Path):
speech_ratio = get_speech_ratio(file)
return file, speech_ratio
def main():
parser = argparse.ArgumentParser(description="Calculate speech ratio.")
parser.add_argument(
"-i", "--input", help="Directory containing audio files", required=True
)
args = parser.parse_args()
if os.path.exists(os.path.join(args.input, "low_speech_ratio")):
logger.info("Low speech ratio directory already exists, skipping...")
exit(0)
data_dir = Path(args.input)
wav_files = list(data_dir.glob("*.wav"))
wav_files.sort()
if len(wav_files) < 100:
logger.warning("Too few files, skipping...")
exit(0)
logger.info(f"Start VAD filtering for {data_dir}...")
results = []
for wav_file in tqdm(wav_files, file=sys.stdout):
speech_ratio = get_speech_ratio(wav_file)
results.append((wav_file, speech_ratio))
results_df = pd.DataFrame(results, columns=["file", "speech_ratio"])
results_df.to_csv(os.path.join(data_dir, "speech_ratio.csv"), index=False)
logger.info(f"Speech ratio stats:\n{results_df['speech_ratio'].describe()}")
threshold = 0.5
low_speech_ratio_dir = os.path.join(data_dir, "low_speech_ratio")
os.makedirs(low_speech_ratio_dir, exist_ok=True)
low_speech_files = results_df[results_df["speech_ratio"] < threshold]["file"]
logger.info(f"Moving {len(low_speech_files)} files to {low_speech_ratio_dir}...")
for low_speech_file in low_speech_files:
shutil.move(low_speech_file, low_speech_ratio_dir)
logger.success("VAD filtering completed.")
if __name__ == "__main__":
main()