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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" | |
# load speech translation checkpoint | |
asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device) | |
# load text-to-speech checkpoint and speaker embeddings | |
processor = SpeechT5Processor.from_pretrained("kfahn/speecht5_finetuned_voxpopuli_es") | |
model = SpeechT5ForTextToSpeech.from_pretrained("kfahn/speecht5_finetuned_voxpopuli_es").to(device) | |
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device) | |
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") | |
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) | |
def translate(audio): | |
outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "es"}) | |
return outputs["text"] | |
def synthesise(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): | |
target_dtype = np.int16 | |
max_range = np.iinfo(target_dtype).max | |
translated_text = translate(audio) | |
synthesised_speech = synthesise(translated_text) | |
synthesised_speech = (synthesised_speech.numpy() * max_range).astype(target_dtype) | |
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 English. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and Microsoft's | |
[SpeechT5 TTS](https://huggingface.co/microsoft/speecht5_tts) model for text-to-speech: | |
![Cascaded STST](https://huggingface.co/datasets/huggingface-course/audio-course-images/resolve/main/s2st_cascaded.png "Diagram of cascaded speech to speech translation") | |
""" | |
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() | |