import tempfile import ffmpeg import asyncio import subprocess import os SAMPLE_RATE = 16000 def convert_audio(data: bytes, out_filename: str): try: with tempfile.NamedTemporaryFile("w+b") as file: file.write(data) file.flush() print(f"Converting media {file.name} to {out_filename}") out, _ = ( ffmpeg.input(file.name, threads=0) .output(out_filename, format="wav", acodec="pcm_s16le", ac=1, ar=SAMPLE_RATE) .overwrite_output() .run(cmd="ffmpeg", capture_stdout=True, capture_stderr=True, input=data) ) except ffmpeg.Error as e: raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e return out MODELS = ["tiny.en", "tiny", "base.en", "base", "small.en", "small", "medium.en", "medium", "large"] class ASR(): def __init__(self, model = "tiny", language = "en"): if model not in MODELS: raise ValueError(f"Invalid model: {model}. Must be one of {MODELS}") self.model = model self.language = language if not os.path.exists("/data/models"): os.mkdir("/data/models") self.model_path = f"/data/models/ggml-{model}.bin" self.model_url = f"https://ggml.ggerganov.com/ggml-model-whisper-{self.model}.bin" self.lock = asyncio.Lock() def load_model(self): if not os.path.exists(self.model_path): print("Downloading model...") subprocess.run(["wget", self.model_url, "-O", self.model_path], check=True) print("Done.") async def transcribe(self, audio: bytes) -> str: filename = tempfile.mktemp(suffix=".wav") convert_audio(audio, filename) async with self.lock: proc = await asyncio.create_subprocess_exec( "./main", "-m", self.model_path, "-l", self.language, "-f", filename, "--no_timestamps", stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE ) stdout, stderr = await proc.communicate() os.remove(filename) if stderr: print(stderr.decode()) text = stdout.decode().strip() print(text) return text