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GPT-SoVITS-v4

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GPT-SoVITS-v4
File size: 4,326 Bytes 
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import argparse
import os
import traceback

os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"

import torch
from faster_whisper import WhisperModel
from tqdm import tqdm

from tools.asr.config import check_fw_local_models

# fmt: off
language_code_list = [
    "af", "am", "ar", "as", "az", 
    "ba", "be", "bg", "bn", "bo", 
    "br", "bs", "ca", "cs", "cy", 
    "da", "de", "el", "en", "es", 
    "et", "eu", "fa", "fi", "fo", 
    "fr", "gl", "gu", "ha", "haw", 
    "he", "hi", "hr", "ht", "hu", 
    "hy", "id", "is", "it", "ja", 
    "jw", "ka", "kk", "km", "kn", 
    "ko", "la", "lb", "ln", "lo", 
    "lt", "lv", "mg", "mi", "mk", 
    "ml", "mn", "mr", "ms", "mt", 
    "my", "ne", "nl", "nn", "no", 
    "oc", "pa", "pl", "ps", "pt", 
    "ro", "ru", "sa", "sd", "si", 
    "sk", "sl", "sn", "so", "sq", 
    "sr", "su", "sv", "sw", "ta", 
    "te", "tg", "th", "tk", "tl", 
    "tr", "tt", "uk", "ur", "uz", 
    "vi", "yi", "yo", "zh", "yue",
    "auto"] 
# fmt: on


def execute_asr(input_folder, output_folder, model_size, language, precision):
    if "-local" in model_size:
        model_size = model_size[:-6]
        model_path = f"tools/asr/models/faster-whisper-{model_size}"
    else:
        model_path = model_size
    if language == "auto":
        language = None  # 不设置语种由模型自动输出概率最高的语种
    print("loading faster whisper model:", model_size, model_path)
    device = "cuda" if torch.cuda.is_available() else "cpu"
    try:
        model = WhisperModel(model_path, device=device, compute_type=precision)
    except:
        return print(traceback.format_exc())

    input_file_names = os.listdir(input_folder)
    input_file_names.sort()

    output = []
    output_file_name = os.path.basename(input_folder)

    for file_name in tqdm(input_file_names):
        try:
            file_path = os.path.join(input_folder, file_name)
            segments, info = model.transcribe(
                audio=file_path,
                beam_size=5,
                vad_filter=True,
                vad_parameters=dict(min_silence_duration_ms=700),
                language=language,
            )
            text = ""

            if info.language == "zh":
                print("检测为中文文本, 转 FunASR 处理")
                if "only_asr" not in globals():
                    from tools.asr.funasr_asr import only_asr  # 如果用英文就不需要导入下载模型
                text = only_asr(file_path, language=info.language.lower())

            if text == "":
                for segment in segments:
                    text += segment.text
            output.append(f"{file_path}|{output_file_name}|{info.language.upper()}|{text}")
        except:
            print(traceback.format_exc())

    output_folder = output_folder or "output/asr_opt"
    os.makedirs(output_folder, exist_ok=True)
    output_file_path = os.path.abspath(f"{output_folder}/{output_file_name}.list")

    with open(output_file_path, "w", encoding="utf-8") as f:
        f.write("\n".join(output))
        print(f"ASR 任务完成->标注文件路径: {output_file_path}\n")
    return output_file_path


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "-i", "--input_folder", type=str, required=True, help="Path to the folder containing WAV files."
    )
    parser.add_argument("-o", "--output_folder", type=str, required=True, help="Output folder to store transcriptions.")
    parser.add_argument(
        "-s",
        "--model_size",
        type=str,
        default="large-v3",
        choices=check_fw_local_models(),
        help="Model Size of Faster Whisper",
    )
    parser.add_argument(
        "-l", "--language", type=str, default="ja", choices=language_code_list, help="Language of the audio files."
    )
    parser.add_argument(
        "-p",
        "--precision",
        type=str,
        default="float16",
        choices=["float16", "float32", "int8"],
        help="fp16, int8 or fp32",
    )

    cmd = parser.parse_args()
    output_file_path = execute_asr(
        input_folder=cmd.input_folder,
        output_folder=cmd.output_folder,
        model_size=cmd.model_size,
        language=cmd.language,
        precision=cmd.precision,
    )