init gsv

bi_v100-gpt-sovits/GPT-SoVITS/tools/asr/fasterwhisper_asr.py

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+import argparse
+import os
+import time
+import traceback
+
+import torch
+from faster_whisper import WhisperModel
+from huggingface_hub import snapshot_download
+from huggingface_hub.errors import LocalEntryNotFoundError
+from tqdm import tqdm
+
+from tools.asr.config import get_models
+from tools.asr.funasr_asr import only_asr
+from tools.my_utils import load_cudnn
+
+# 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 download_model(model_size: str):
+    if "distil" in model_size:
+        repo_id = "Systran/faster-{}-whisper-{}".format(*model_size.split("-", maxsplit=1))
+    else:
+        repo_id = f"Systran/faster-whisper-{model_size}"
+    model_path = f"tools/asr/models/{repo_id.strip('Systran/')}"
+
+    files: list[str] = [
+        "config.json",
+        "model.bin",
+        "tokenizer.json",
+        "vocabulary.txt",
+    ]
+    if model_size == "large-v3" or "distil" in model_size:
+        files.append("preprocessor_config.json")
+        files.append("vocabulary.json")
+
+        files.remove("vocabulary.txt")
+
+    for attempt in range(2):
+        try:
+            snapshot_download(
+                repo_id=repo_id,
+                allow_patterns=files,
+                local_dir=model_path,
+            )
+            break
+        except LocalEntryNotFoundError:
+            if attempt < 1:
+                time.sleep(2)
+            else:
+                print("[ERROR] LocalEntryNotFoundError and no fallback.")
+                traceback.print_exc()
+                exit(1)
+        except Exception as e:
+            print(f"[ERROR] Unexpected error on attempt {attempt + 1}: {e}")
+            traceback.print_exc()
+            exit(1)
+
+    return model_path
+
+
+def execute_asr(input_folder, output_folder, model_path, language, precision):
+    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_path, model_path)
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model = WhisperModel(model_path, device=device, compute_type=precision)
+
+    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")
+                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 Exception as e:
+            print(e)
+            traceback.print_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
+
+
+load_cudnn()
+
+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=get_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()
+    model_size = cmd.model_size
+    if model_size == "large":
+        model_size = "large-v3"
+    model_path = download_model(model_size)
+    output_file_path = execute_asr(
+        input_folder=cmd.input_folder,
+        output_folder=cmd.output_folder,
+        model_path=model_path,
+        language=cmd.language,
+        precision=cmd.precision,
+    )