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import gradio as gr
import torch
from transformers import pipeline
from timestamp import format_timestamp
MODEL_NAME = "openai/whisper-medium"
BATCH_SIZE = 8
device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
def transcribe(file, task, return_timestamps):
outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)
text = outputs["text"]
timestamps = outputs["chunks"]
if return_timestamps==True:
timestamps = [f"[{format_timestamp(chunk['timestamp'][0])} -> {format_timestamp(chunk['timestamp'][1])}] {chunk['text']}" for chunk in timestamps]
else:
timestamps = [f"{chunk['text']}" for chunk in timestamps]
text = "\n".join(str(feature) for feature in timestamps)
return text
file_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="upload", optional=True, label="Audio file", type="filepath"),
gr.inputs.Radio(["transcribe"], label="Task", default="transcribe"),
gr.inputs.Checkbox(default=False, label="Return timestamps"),
],
outputs="text",
layout="horizontal",
theme="huggingface",
title="Whisper Demo: Transcribe Audio",
description=(
"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the"
f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
" of arbitrary length."
),
cache_examples=True,
allow_flagging="never",
)
file_transcribe.launch(enable_queue=True, debug = True) |