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import torch
import torchaudio
from pathlib import Path
import soundfile as sf
from typing import Any
from config import TARGET_SR, SUPPORTED_EXTS
def transcribe_file(path: str | Path, pipe: Any) -> str:
"""
Transcribe an audio file to text using a given ASR pipeline.
Args:
path: Path or string pointing to an audio file.
asr_pipeline: A Hugging Face transformers pipeline object for
automatic-speech-recognition. Should accept a numpy
array and return a dict with key 'text'.
Returns:
The transcribed text as returned by the pipeline.
Raises:
ValueError: If loading or decoding the audio fails.
"""
speech = load_resample(path)
return pipe(speech.numpy())["text"] # type: ignore[index]
def load_resample(path: str | Path, target_sr: int = TARGET_SR) -> torch.Tensor:
"""
Load an audio file and resample it to the target sample rate, returning
a mono torch.Tensor.
Args:
path: Path or string pointing to an audio file.
target_sr: Desired sample rate (in Hz). Defaults to TARGET_SR from config.
Returns:
A 1-D torch.Tensor of dtype float32 sampled at target_sr.
Raises:
ValueError: If the file extension is not in SUPPORTED_EXTS.
ValueError: If the audio file cannot be decoded.
"""
ext = Path(path).suffix.lower()
if ext not in SUPPORTED_EXTS:
raise ValueError(
f"Unsupported file-type “{ext or 'unknown'}”. Please upload WAV, FLAC, MP3, OGG/Opus or M4A."
)
try:
speech, sr = sf.read(str(path))
except RuntimeError as exc:
raise ValueError(
"Couldn't decode the audio file - maybe it's corrupted or in an uncommon codec."
) from exc
speech = torch.tensor(speech).float()
if speech.ndim == 2: # stereo to mono
speech = speech.mean(dim=1)
if sr != target_sr:
speech = torchaudio.functional.resample(
speech, orig_freq=sr, new_freq=target_sr
)
return speech
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