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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-large-v2", device=device)
# load text-to-speech checkpoint and speaker embeddings
model_id = "Sandiago21/speecht5_finetuned_mozilla_foundation_common_voice_13_german" # update with your model id
# pipe = pipeline("automatic-speech-recognition", model=model_id)
model = SpeechT5ForTextToSpeech.from_pretrained(model_id)
processor = SpeechT5Processor.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)
replacements = [
("Ä", "E"),
("Æ", "E"),
("Ç", "C"),
("É", "E"),
("Í", "I"),
("Ó", "O"),
("Ö", "E"),
("Ü", "Y"),
("ß", "S"),
("à", "a"),
("á", "a"),
("ã", "a"),
("ä", "e"),
("å", "a"),
("ë", "e"),
("í", "i"),
("ï", "i"),
("ð", "o"),
("ñ", "n"),
("ò", "o"),
("ó", "o"),
("ô", "o"),
("ö", "u"),
("ú", "u"),
("ü", "y"),
("ý", "y"),
("Ā", "A"),
("ā", "a"),
("ă", "a"),
("ą", "a"),
("ć", "c"),
("Č", "C"),
("č", "c"),
("ď", "d"),
("Đ", "D"),
("ę", "e"),
("ě", "e"),
("ğ", "g"),
("İ", "I"),
("О", "O"),
("Ł", "L"),
("ń", "n"),
("ň", "n"),
("Ō", "O"),
("ō", "o"),
("ő", "o"),
("ř", "r"),
("Ś", "S"),
("ś", "s"),
("Ş", "S"),
("ş", "s"),
("Š", "S"),
("š", "s"),
("ū", "u"),
("ź", "z"),
("Ż", "Z"),
("Ž", "Z"),
("ǐ", "i"),
("ǐ", "i"),
("ș", "s"),
("ț", "t"),
]
def cleanup_text(text):
for src, dst in replacements:
text = text.replace(src, dst)
return text
def transcribe_to_german(audio):
outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "german"})
return outputs["text"]
def synthesise_from_german(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 = transcribe_to_german(audio)
synthesised_speech = synthesise_from_german(translated_text)
synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
return ((16000, synthesised_speech), translated_text)
title = "Cascaded STST"
description = """
Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in German. Demo uses OpenAI's [Whisper Large v2](https://huggingface.co/openai/whisper-large-v2) model for speech translation, and [Sandiago21/speecht5_finetuned_mozilla_foundation_common_voice_13_german](https://huggingface.co/Sandiago21/speecht5_finetuned_mozilla_foundation_common_voice_13_german) 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 German 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"), gr.outputs.Textbox()],
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"), gr.outputs.Textbox()],
examples=[["./example.wav"]],
title=title,
description=description,
)
with demo:
gr.TabbedInterface([mic_translate, file_translate], ["Microphone", "Audio File"])
demo.launch()
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