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import gradio as gr
import numpy as np
import torch
from datasets import load_dataset
from transformers import pipeline, VitsModel, AutoTokenizer
device = "cuda:0" if torch.cuda.is_available() else "cpu"
# load speech translation checkpoint
asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-small", device=device)
# load facebook mms espanish model/checkpoint
model = VitsModel.from_pretrained("facebook/mms-tts-spa")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-spa")
target_dtype = np.int16
max_range = np.iinfo(target_dtype).max
def translate(audio):
outputs = asr_pipe(
audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "es"}
)
return outputs["text"]
def synthesise(text):
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
speech = model(**inputs).waveform
return speech.squeeze(0).cpu()
def speech_to_speech_translation(audio):
translated_text = translate(audio)
synthesised_speech = synthesise(translated_text)
synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16)
return 16_000, synthesised_speech
title = "Cascaded STST"
description = """
Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in *Spanish*. Demo uses OpenAI's [Whisper Small](https://huggingface.co/openai/whisper-small) model for speech translation, and Meta's
[MMS TTS Spanish](https://huggingface.co/facebook/mms-tts-spa) model for text-to-speech:
"""
demo = gr.Blocks()
mic_translate = gr.Interface(
fn=speech_to_speech_translation,
inputs=gr.Audio(source="microphone", type="filepath"),
outputs=gr.Audio(label="Generated Speech :)", type="numpy"),
title=title,
description=description,
)
file_translate = gr.Interface(
fn=speech_to_speech_translation,
inputs=gr.Audio(source="upload", type="filepath"),
outputs=gr.Audio(label="Generated Speech", type="numpy"),
examples=[["./example.wav"]],
title=title,
description=description,
)
with demo:
gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
demo.launch(debug=True)
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