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import torch
import gradio as gr
import pytube as pt
from transformers import pipeline
from fastspeech2 import FastSpeech2
MODEL_NAME = "openai/whisper-large-v2"
device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
all_special_ids = pipe.tokenizer.all_special_ids
transcribe_token_id = all_special_ids[-5]
translate_token_id = all_special_ids[-6]
voice_conversion_model = FastSpeech2.from_pretrained("path/to/pretrained/voice_conversion_model")
def convert_voice(text):
converted_voice = voice_conversion_model(text)
return converted_voice
def transcribe(microphone, state, task="transcribe"):
file = microphone
pipe.model.config.forced_decoder_ids = [[2, transcribe_token_id if task=="transcribe" else translate_token_id]]
text = pipe(file)["text"]
converted_voice = convert_voice(text)
return state + "\n" + converted_voice, state + "\n" + converted_voice, converted_voice
mf_transcribe = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(source="microphone", type="filepath", optional=True),
gr.State(value="")
],
outputs=[
gr.Textbox(lines=15),
gr.State(),
gr.Audio(type="auto") # Add this line to include the converted voice as an output
],
layout="horizontal",
theme="huggingface",
title="Whisper Large V2: Transcribe Audio and Voice Conversion",
live=True,
description=(
"Transcribe long-form microphone or audio inputs and convert the voice 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 and FastSpeech2 for voice conversion."
),
allow_flagging="never",
)
mf_transcribe.launch(enable_queue=True)
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