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import torch | |
from transformers import pipeline, VitsModel, VitsTokenizer | |
import numpy as np | |
import gradio as gr | |
device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
# Load Whisper-small | |
pipe = pipeline("automatic-speech-recognition", | |
model="openai/whisper-small", | |
device=device | |
) | |
# Load the model checkpoint and tokenizer | |
#model = VitsModel.from_pretrained("Matthijs/mms-tts-fra") | |
#tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-fra") | |
model = VitsModel.from_pretrained("facebook/mms-tts-fra") | |
tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-fra") | |
# Define a function to translate an audio, in english here | |
def translate(audio): | |
outputs = pipe(audio, max_new_tokens=256, | |
generate_kwargs={"task": "transcribe", "language": "eng"}) | |
return outputs["text"] | |
# Define function to generate the waveform output | |
def synthesise(text): | |
inputs = tokenizer(text, return_tensors="pt") | |
input_ids = inputs["input_ids"] | |
with torch.no_grad(): | |
outputs = model(input_ids) | |
return outputs.audio[0] | |
# Define the pipeline | |
def speech_to_speech_translation(audio): | |
translated_text = translate(audio) | |
synthesised_speech = synthesise(translated_text) | |
synthesised_speech = ( | |
synthesised_speech.numpy() * 32767).astype(np.int16) | |
return 16000, synthesised_speech | |
def predict(transType, language, audio, audio_mic = None): | |
if not audio and audio_mic: | |
audio = audio_mic | |
if transType == "Text": | |
return translate(audio) | |
if transType == "Audio": | |
return speech_to_speech_translation(audio) | |
# Define the title etc | |
title = "Swedish STSOT (Speech To Speech Or Text)" | |
description="Use Whisper pretrained model to convert swedish audio to english (text or audio)" | |
demo = gr.Blocks() | |
supportLangs = ["Swedish", "French (in training)"] | |
transTypes = ["Text", "Audio"] | |
demo = gr.Interface( | |
fn=predict, | |
inputs=[ | |
gr.Radio(label="Choose your output format", choices=transTypes), | |
gr.Radio(label="Choose a source language", choices=supportLangs, value="Swedish"), | |
gr.Audio(label="Import an audio", source="upload", type="numpy"), | |
gr.Audio(label="Record an audio", source="microphone", type="numpy"), | |
], | |
outputs=[ | |
gr.Text(label="Translation"), | |
], | |
title=title, | |
description=description, | |
article="", | |
examples=[], | |
).launch() | |
demo.launch() |