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speech-to-speech-translation / app.py

NicolasDenier

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


	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("Sandiago21/speecht5_finetuned_facebook_voxpopuli_french") # "microsoft/speecht5_tts"

	model = SpeechT5ForTextToSpeech.from_pretrained("Sandiago21/speecht5_finetuned_facebook_voxpopuli_french").to(device)
	vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)

	def npy_loader(path):
	np_sample = np.transpose(np.load(path))
	sample = torch.from_numpy(np_sample)
	return sample

	embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation", streaming=True)
	speaker_embeddings = torch.tensor(next(iter(embeddings_dataset))["xvector"]).unsqueeze(0)

	#xvector_path = "xvectors/french_recording-bernard-candide_segment_182.npy"
	#speaker_embeddings = torch.tensor(npy_loader(xvector_path)[0]).unsqueeze(0)
	#print("speaker_embeddings shape", speaker_embeddings.shape)


	def translate(audio):
	outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "fr"})
	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):
	translated_text = translate(audio)
	#print("translated_text", translated_text)
	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 french. 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 [finetuned for french language](https://huggingface.co/Sandiago21/speecht5_finetuned_facebook_voxpopuli_french):

	![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()