Feat: slice-and-transcribe

transcribe.py

@@ -0,0 +1,51 @@
+import argparse
+import os
+import sys
+
+from faster_whisper import WhisperModel
+from tqdm import tqdm
+
+
+def transcribe(wav_path, initial_prompt=None):
+    segments, _ = model.transcribe(
+        wav_path, beam_size=5, language="ja", initial_prompt=initial_prompt
+    )
+    texts = [segment.text for segment in segments]
+    return "".join(texts)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input_dir", type=str, default="raw")
+    parser.add_argument("--output_file", type=str, default="esd.list")
+    parser.add_argument(
+        "--initial_prompt", type=str, default="こんにちは。元気、ですかー？私は……ちゃんと元気だよ！"
+    )
+    parser.add_argument("--speaker_name", type=str, default=None, required=True)
+    parser.add_argument("--model", type=str, default="large-v3")
+
+    args = parser.parse_args()
+
+    speaker_name = args.speaker_name
+
+    input_dir = args.input_dir
+    output_file = args.output_file
+    initial_prompt = args.initial_prompt
+
+    model = WhisperModel("large-v3", device="cuda", compute_type="bfloat16")
+
+    wav_files = [
+        os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith(".wav")
+    ]
+    if os.path.exists(output_file):
+        print(f"{output_file}が存在するので、バックアップを{output_file}.bakに作成します。")
+        if os.path.exists(output_file + ".bak"):
+            print(f"{output_file}.bakも存在するので、削除します。")
+            os.remove(output_file + ".bak")
+        os.rename(output_file, output_file + ".bak")
+
+    with open(output_file, "w", encoding="utf-8") as f:
+        for wav_file in tqdm(wav_files, file=sys.stdout):
+            file_name = os.path.basename(wav_file)
+            text = transcribe(wav_file, initial_prompt=initial_prompt)
+            f.write(f"{file_name}|{speaker_name}|JP|{text}\n")