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import torch
from langchain_core.tools import tool
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from .data_helpers import get_file_path
@tool(parse_docstring=True)
def transcribe_audio_file(file_name: str) -> str:
"""
Transcribes an audio file to text.
Args:
file_name: The name of the audio file. This is simply the file name,
not the full path.
Returns:
The transcribed text.
"""
# Specific setting for local run with GPU busy for the LLM (ollama)
cuda_available = False
device = "cuda:0" if cuda_available else "cpu"
torch_dtype = torch.float16 if cuda_available else torch.float32
model_id = "openai/whisper-large-v3-turbo"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id,
torch_dtype=torch_dtype,
low_cpu_mem_usage=True,
use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
torch_dtype=torch_dtype,
device=device,
)
generate_kwargs = {
"return_timestamps": True,
}
file_path = get_file_path(file_name)
result = pipe(file_path, generate_kwargs=generate_kwargs)
return result["text"]
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