Added GPT-SoVITS cli with Docker

2026-11-02 18:09:20 +00:00 · 2024-07-16 17:10:28 +05:30
parent a53d86ed76
commit bede4642bb
36 changed files with 2036 additions and 4545 deletions
@@ -0,0 +1,66 @@
+import os
+import traceback
+import torch
+
+from transformers import pipeline
+import soundfile as sf
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+to_lang_name = {
+    "en": "english",
+    "ja": "japanese",
+    "zh":"chinese"
+}
+    
+def slice(input_audio, audio_output_folder, transcript_output_folder, model_size, language, precision):
+    audio_data, sr = sf.read(input_audio)
+    
+    output = []
+    os.makedirs(audio_output_folder, exist_ok=True)
+    os.makedirs(transcript_output_folder, exist_ok=True)
+    output_base_name = os.path.splitext(os.path.basename(input_audio))[0]
+    
+    try:
+        # hf whisper does better for longer audios
+        print(f"Loading whisper {model_size} model...")
+        whisper_model = pipeline(
+            "automatic-speech-recognition",
+            model=f"openai/whisper-{model_size}",
+            chunk_length_s=30,
+            return_timestamps=True,
+            device=device,
+        )
+        
+        print("Trancribing the audio...")
+        segments = whisper_model(input_audio, return_timestamps=True, generate_kwargs={"language": to_lang_name[language]})
+        skipped_segments = 0
+        
+        print("Slicing audio based on transcript...")
+        for i, segment in enumerate(segments["chunks"]):
+            output_audio_path = os.path.join(audio_output_folder, f"{output_base_name}_{i}.wav")
+            start_time = int(segment["timestamp"][0]*sr)
+            end_time = int(segment["timestamp"][1]*sr)
+            
+            if not 54 > (segment["timestamp"][1]-segment["timestamp"][0]) > 0.6:
+                print(f"Skipped segment {i} due to being under or over the required length")
+                skipped_segments += 1
+                continue
+                
+            new_audio_data = audio_data[start_time:end_time]
+            sf.write(
+                output_audio_path,
+                new_audio_data,
+                sr,
+                format="wav"
+            )
+            
+            output.append(f"{output_audio_path}|{output_base_name}|{language.upper()}|{segment['text']}")
+            print(f"{i}. [{segment['timestamp'][0]:.2f}s -> {segment['timestamp'][1]:.2f}s]{segment['text']}")
+    except:
+        return print(traceback.format_exc())
+    
+    print(f"Total skipped segments: {skipped_segments}")
+    output_transcript_path = os.path.join(transcript_output_folder, f"{output_base_name}.list")
+    with open(output_transcript_path, "w", encoding="utf-8") as f:
+        f.write("\n".join(output))
+    return audio_output_folder, output_transcript_path