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speech-to-speech-translation / app.py

mitro99

Update app.py

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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, 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)

	model = VitsModel.from_pretrained('facebook/mms-tts-ron')
	tokenizer = VitsTokenizer.from_pretrained('facebook/mms-tts-ron')

	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": "ro"})
	return outputs["text"]


	def synthesise(text):
	inputs = tokenizer(text=text, return_tensors="pt")
	# speech = model.generate_speech(inputs["input_ids"].to(device), speaker_embeddings.to(device), vocoder=vocoder)
	with torch.no_grad():
	tts_output = model(inputs['input_ids'].to(device))

	speech = tts_output['waveform']
	return speech.cpu()


	def speech_to_speech_translation(audio):
	translated_text = translate(audio)
	print(translated_text)
	synthesised_speech = synthesise(translated_text)
	synthesised_speech = (synthesised_speech.numpy() * 32767).astype(np.int16)
	return 16000, synthesised_speech.T


	title = "Cascaded STST"
	description = """
	Demo for cascaded speech-to-speech translation (STST), mapping from source speech in any language to target speech in Romanian. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech translation, and Facebook's
	[MMS-TTS-RON](https://huggingface.co/facebook/mms-tts-ron) 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()