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- import ffmpeg
- import asyncio
- import subprocess
- import os
- SAMPLE_RATE = 16000
- def convert_audio(data: bytes) -> bytes:
- try:
- # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
- # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
- out, _ = (
- ffmpeg.input("pipe:", threads=0)
- .output("audio.wav", format="wav", acodec="pcm_s16le", ac=1, ar=SAMPLE_RATE)
- .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:
- async with self.lock:
- convert_audio(audio)
- proc = await asyncio.create_subprocess_exec(
- "./main",
- "-m", self.model_path,
- "-l", self.language,
- "-f", "audio.wav",
- "--no_timestamps",
- stdout=asyncio.subprocess.PIPE,
- stderr=asyncio.subprocess.PIPE
- )
- stdout, stderr = await proc.communicate()
- os.remove("audio.wav")
- if stderr:
- print(stderr.decode())
-
- text = stdout.decode()
- print(text)
- return text
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