| """Transcribe an audio file with Anis Transcribe 1.0 (ONNX int8, CPU). |
| |
| pip install onnxruntime "transformers>=5.19" soundfile numpy |
| python transcribe.py recording.wav # any sample rate / channels; ffmpeg-decodable formats need ffmpeg |
| |
| Greedy CTC decoding, then spoken numbers -> digits (numwords.itn). Audio is read in 25 s windows, as in training. |
| """ |
| import json |
| import subprocess |
| import sys |
| from pathlib import Path |
|
|
| import numpy as np |
| import onnxruntime as ort |
| import soundfile as sf |
| from transformers import AutoFeatureExtractor |
|
|
| import numwords |
|
|
| HERE = Path(__file__).parent |
| SR, WINDOW = 16000, 25 * 16000 |
|
|
|
|
| def read16k(path: str) -> np.ndarray: |
| try: |
| wav, sr = sf.read(path, dtype="float32") |
| if wav.ndim > 1: |
| wav = wav.mean(1) |
| if sr == SR: |
| return wav |
| except Exception: |
| pass |
| raw = subprocess.run(["ffmpeg", "-v", "error", "-i", path, "-ac", "1", "-ar", str(SR), "-f", "f32le", "-"], |
| check=True, capture_output=True).stdout |
| return np.frombuffer(raw, dtype=np.float32) |
|
|
|
|
| class AnisTranscribe: |
| def __init__(self, threads: int = 4): |
| so = ort.SessionOptions() |
| so.intra_op_num_threads = threads |
| self.sess = ort.InferenceSession(str(HERE / "anis-transcribe-1.0-int8.onnx"), so, providers=["CPUExecutionProvider"]) |
| self.fe = AutoFeatureExtractor.from_pretrained(str(HERE)) |
| self.vocab = json.loads((HERE / "vocab.json").read_text(encoding="utf-8")) |
|
|
| def _greedy(self, lp: np.ndarray) -> str: |
| prev, out = 0, [] |
| for t in lp.argmax(-1): |
| if t != prev and t != 0: |
| out.append(self.vocab[t]) |
| prev = t |
| return " ".join("".join(out).split()) |
|
|
| def __call__(self, wav: np.ndarray) -> str: |
| parts = [] |
| for s in range(0, max(1, len(wav)), WINDOW): |
| seg = wav[s:s + WINDOW] |
| if len(seg) < SR // 10: |
| continue |
| f = self.fe([seg], sampling_rate=SR, return_tensors="np", padding="longest") |
| lp = self.sess.run(None, {"features": f["input_features"].astype(np.float32)})[0][0] |
| parts.append(self._greedy(lp)) |
| return numwords.itn(" ".join(p for p in parts if p)) |
|
|
|
|
| if __name__ == "__main__": |
| model = AnisTranscribe() |
| for path in sys.argv[1:]: |
| print(model(read16k(path))) |
|
|
