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from transformers import pipeline
import torch
import gc
import librosa
from model_api import clear_gpu_cache, get_device_and_dtype
def transcribe_audio(audio_path: str, device: str = "cuda", torch_dtype: torch.dtype = torch.float16) -> str:
"""
Transcribes an MP3 audio file.
Args:
audio_path: Path to the audio file.
device: The device to use for transcription (e.g., "cuda" for GPU, "cpu" for CPU).
torch_dtype: The torch data type to use for model computations.
Returns:
The transcribed text.
"""
try:
# Create a pipeline with explicit device specification
pipe = pipeline(
"automatic-speech-recognition",
model="openai/whisper-small",
device=device,
chunk_length_s=25,
stride_length_s=2,
torch_dtype=torch_dtype,
)
# Perform transcription
result = pipe(audio_path, return_timestamps=True)
# Extract text
text = result['text']
return text
except Exception as e:
print(f"Error during transcription: {str(e)}")
return ""
finally:
del pipe
clear_gpu_cache()
if __name__ == "__main__":
selected_device, selected_dtype = get_device_and_dtype()
result = transcribe_audio("/workspaces/Video_Analyser/app_srv/downloads/45677153-510d-4f47-95ee-c1b4b0843433/audio.mp3.mp3", selected_device, selected_dtype)
print(result) |