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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
## Imports for MMS
from transformers import VitsModel, VitsTokenizer
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("microsoft/speecht5_tts")
# model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts").to(device)
# vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
# For Dutch
##### speecht5 #####
# model_id = 'sanchit-gandhi/speecht5_tts_vox_nl'
# processor = SpeechT5Processor.from_pretrained(model_id)
# model = SpeechT5ForTextToSpeech.from_pretrained(model_id)
# vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
##### mms #####
model = VitsModel.from_pretrained("Matthijs/mms-tts-nld")
tokenizer = VitsTokenizer.from_pretrained("Matthijs/mms-tts-nld")
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)
# Original
# def translate(audio):
# outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"})
# return outputs["text"]
# Dutch
def translate(audio):
outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "nl"})
return outputs["text"]
# Original
# 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 synthesise(text):
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
outputs = model(inputs["input_ids"])
speech = outputs.audio[0]
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 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:
# 
# """
description = """
# Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in Dutch. 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:
# 
# """
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()
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