mARTin
60db1b181f
Run Qwen3-TTS with CUDA graph acceleration on NVIDIA DGX Spark (ARM64 / SM 121 / CUDA 13) as an OpenAI-compatible TTS API server. - Dockerfile targeting nvidia/cuda:13.0.2-base-ubuntu24.04 with ARM64 cu130 PyTorch wheels - Patch for max-seq-len support to handle long reference audio without crashes - OpenWebUI + SillyTavern compatible API endpoints (/v1/models, /v1/audio/voices, /speakers) - Voice management: auto-generate voices.json from speaker reference audio files - Auto-transcription helper script for generating reference text from audio Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
94 lines
3.3 KiB
Python
94 lines
3.3 KiB
Python
"""
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Batch-transcribe speaker reference audio files using a local Whisper-compatible API.
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Creates .reference.txt files alongside each audio file in the speakers directory.
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These transcriptions are used by generate_voices.py to build the voice registry.
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Usage:
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python auto_transcribe.py [--api-url http://localhost:8010/v1/audio/transcriptions]
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IMPORTANT: Reference audio for voice cloning should be 5-15 seconds long.
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Longer files will produce poor cloning results and slow down inference.
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"""
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import os
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import sys
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import json
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import argparse
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import requests
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def main():
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parser = argparse.ArgumentParser(description="Batch-transcribe speaker reference audio")
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parser.add_argument("--api-url", default="http://localhost:8010/v1/audio/transcriptions",
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help="Whisper-compatible transcription API URL")
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parser.add_argument("--speaker-dir", default="./speakers",
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help="Directory containing speaker audio files")
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parser.add_argument("--model", default="whisper-1",
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help="Transcription model name")
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args = parser.parse_args()
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speaker_dir = args.speaker_dir
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# Verify API is reachable
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try:
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requests.get(args.api_url.rsplit('/', 2)[0], timeout=5)
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except requests.ConnectionError:
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print(f"Error: Cannot reach transcription API at {args.api_url}")
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print("Make sure your Whisper/ASR service is running.")
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sys.exit(1)
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if not os.path.exists(speaker_dir):
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print(f"Error: Speaker directory not found: {speaker_dir}")
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sys.exit(1)
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audio_files = [f for f in os.listdir(speaker_dir)
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if f.endswith(('.wav', '.mp3')) and not f.startswith('.')]
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print(f"Found {len(audio_files)} audio files in {speaker_dir}")
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for filename in sorted(audio_files):
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base_name = os.path.splitext(filename)[0]
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ref_txt_path = os.path.join(speaker_dir, f"{base_name}.reference.txt")
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if os.path.exists(ref_txt_path):
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print(f" Skipping {filename} (already transcribed)")
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continue
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filepath = os.path.join(speaker_dir, filename)
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print(f" Transcribing {filename}...", end=" ", flush=True)
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try:
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with open(filepath, 'rb') as f:
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response = requests.post(
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args.api_url,
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files={"file": (filename, f)},
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data={"model": args.model},
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timeout=60,
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)
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if response.status_code == 200:
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# Handle both JSON and plain text responses
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try:
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text = response.json().get("text", "").strip()
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except (json.JSONDecodeError, AttributeError):
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text = response.text.strip()
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if text:
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with open(ref_txt_path, 'w', encoding='utf-8') as f:
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f.write(text)
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print(f"OK ({len(text)} chars)")
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else:
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print("EMPTY (no speech detected)")
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else:
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print(f"FAILED (HTTP {response.status_code})")
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except requests.Timeout:
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print("TIMEOUT")
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except Exception as e:
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print(f"ERROR: {e}")
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print("Done.")
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if __name__ == "__main__":
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main()
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