79 lines
3.0 KiB
Python
79 lines
3.0 KiB
Python
#!/usr/bin/env python3
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"""
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SNAC audio encoder -- turns a real audio/video file into `<snac_N>` tokens
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(listen format, 3 tokens/base-frame), the reverse of tools/decode/decode_snac.py.
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Reuses the exact encode_listen() logic already in pipeline_pose/snac_finevideo.py
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(unchanged since inception) rather than re-deriving it.
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This model (vla-1.7b-qwen3-v2) only ever saw listen-format audio wrapped in
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the generic <snac> tag -- NOT the newer (2026-07-23) <listen>/<speak>
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convention or the speak-format L2 tokens. Output here matches that: always
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listen-format, always <snac> wrapper.
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Usage:
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python tools/encode/encode_snac.py --input clip.wav --output tokens.txt
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python tools/encode/encode_snac.py --input video.mp4 --output tokens.txt
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# any format ffmpeg can read (audio extracted automatically, works on
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# video files too -- just uses the audio track)
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"""
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import argparse
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import os
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import subprocess
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import sys
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sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "..", "pipeline_pose"))
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SAMPLE_RATE = 24000
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SNAC_MODEL = "hubertsiuzdak/snac_24khz"
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def _get_ffmpeg() -> str:
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import imageio_ffmpeg
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return imageio_ffmpeg.get_ffmpeg_exe()
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def extract_audio(input_path: str):
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import numpy as np
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cmd = [_get_ffmpeg(), "-y", "-i", input_path, "-vn", "-ac", "1", "-ar", str(SAMPLE_RATE), "-f", "f32le", "-"]
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result = subprocess.run(cmd, capture_output=True, timeout=300)
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if result.returncode != 0 or not result.stdout:
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raise RuntimeError(f"ffmpeg failed to extract audio from {input_path}: {result.stderr.decode(errors='replace')[:500]}")
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audio = np.frombuffer(result.stdout, dtype=np.float32).copy()
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if len(audio) == 0:
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raise RuntimeError(f"No audio extracted from {input_path} -- does it have an audio track?")
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return audio
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def encode_file(input_path: str) -> list:
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import torch
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from snac import SNAC
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from snac_finevideo import encode_listen # reused verbatim, not re-derived
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audio = extract_audio(input_path)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = SNAC.from_pretrained(SNAC_MODEL).eval().to(device)
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tokens = encode_listen(audio, model, device) # already "<snac_N>" strings, listen-format
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return tokens
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def main():
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ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
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ap.add_argument("--input", required=True, help="Audio or video file (any ffmpeg-readable format)")
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ap.add_argument("--output", help="Optional: write the <snac> block to this file")
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args = ap.parse_args()
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tokens = encode_file(args.input)
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duration_s = len(tokens) / 3 / 12.5
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print(f"{len(tokens)} snac tokens ({len(tokens) // 3} base frames, ~{duration_s:.2f}s @ 12.5Hz base rate)")
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block = "<snac> " + " ".join(tokens) + " </snac>"
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print(block)
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if args.output:
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with open(args.output, "w") as f:
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f.write(block)
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print(f"Saved: {args.output}")
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if __name__ == "__main__":
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main()
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