|
import gradio as gr |
|
import numpy as np |
|
import torch |
|
from datasets import load_dataset |
|
from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline |
|
|
|
|
|
device = "cuda:0" if torch.cuda.is_available() else "cpu" |
|
|
|
|
|
asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v2", device=device) |
|
|
|
|
|
model_id = "Sandiago21/speecht5_finetuned_voxpopuli_it" |
|
|
|
model = SpeechT5ForTextToSpeech.from_pretrained(model_id) |
|
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") |
|
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") |
|
speaker_embeddings = torch.tensor(embeddings_dataset[7440]["xvector"]).unsqueeze(0) |
|
|
|
processor = SpeechT5Processor.from_pretrained(model_id) |
|
|
|
replacements = [ |
|
("á", "a"), |
|
("ç", "c"), |
|
("è", "e"), |
|
("ì", "i"), |
|
("í", "i"), |
|
("ò", "o"), |
|
("ó", "o"), |
|
("ù", "u"), |
|
("ú", "u"), |
|
("š", "s"), |
|
("ï", "i"), |
|
] |
|
|
|
def cleanup_text(text): |
|
for src, dst in replacements: |
|
text = text.replace(src, dst) |
|
return text |
|
|
|
def synthesize_speech(text): |
|
text = cleanup_text(text) |
|
inputs = processor(text=text, return_tensors="pt") |
|
speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder) |
|
|
|
return gr.Audio.update(value=(16000, speech.cpu().numpy())) |
|
|
|
def translate(audio): |
|
outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "italian"}) |
|
return outputs["text"] |
|
|
|
|
|
def synthesise(text): |
|
text = cleanup_text(text) |
|
inputs = processor(text=text, return_tensors="pt") |
|
speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder) |
|
return speech.cpu() |
|
|
|
|
|
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 |
|
|
|
|
|
title = "Cascaded STST" |
|
description = """ |
|
Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in Italian. Demo uses OpenAI's [Whisper Large v2](https://huggingface.co/openai/whisper-large-v2) model for speech translation, and [Sandiago21/speecht5_finetuned_voxpopuli_it](https://huggingface.co/Sandiago21/speecht5_finetuned_voxpopuli_it) checkpoint for text-to-speech, which is based on Microsoft's |
|
[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech, fine-tuned in Italian Audio dataset: |
|
 |
|
""" |
|
|
|
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() |
|
|
