|
import os |
|
|
|
os.system("wget https://www.isi.edu/~ulf/uroman/downloads/uroman-v1.2.7.tar.gz") |
|
os.system("mkdir uroman") |
|
os.system("tar -zxvf ./uroman-v1.2.7.tar.gz -C ./uroman") |
|
os.system("chmod +x ./uroman/bin/uroman.pl") |
|
|
|
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="KoRiF/whisper-small-be", device=device) |
|
|
|
|
|
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") |
|
|
|
model = SpeechT5ForTextToSpeech.from_pretrained("KoRiF/speecht5_finetuned_common_voice_be").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, transliteration = lambda txt: txt): |
|
outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "be"}) |
|
return transliteration(outputs["text"]) |
|
|
|
import subprocess |
|
|
|
def transliterate_text(text, lang_code=None, use_chart=False, use_cache=True): |
|
command = ['perl', './uroman/bin/uroman.pl'] |
|
if lang_code: |
|
command.extend(['-l', lang_code]) |
|
if use_chart: |
|
command.append('--chart') |
|
if not use_cache: |
|
command.append('--no-cache') |
|
|
|
process = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, |
|
universal_newlines=True) |
|
output, error = process.communicate(input=text) |
|
if (error): |
|
print(f"Error: >>> {error}") |
|
return output.strip() |
|
|
|
language = 'bel' |
|
def transliterate(text): |
|
return transliterate_text(text, language) |
|
|
|
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() |
|
|
|
|
|
|
|
target_dtype = np.int16 |
|
max_range = np.iinfo(target_dtype).max |
|
|
|
def speech_to_speech_translation(audio): |
|
translated_text = translate(audio, transliterate) |
|
synthesised_speech = synthesise(translated_text) |
|
synthesised_speech = (synthesised_speech.numpy() * max_range).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: |
|
|
|
![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"), |
|
|
|
title=title, |
|
description=description, |
|
) |
|
|
|
with demo: |
|
gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"]) |
|
|
|
demo.launch() |
|
|