import gradio as gr import numpy as np import torch from datasets import load_dataset from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline from transformers import M2M100ForConditionalGeneration from small100.tokenization_small100 import SMALL100Tokenizer 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, generate_kwargs = {"task": "translate"}) model = M2M100ForConditionalGeneration.from_pretrained("alirezamsh/small100") tokenizer = M2M100Tokenizer.from_pretrained("alirezamsh/small100", tgt_lang="sw") # load text-to-speech checkpoint and speaker embeddings # processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") processor = SpeechT5Processor.from_pretrained("samuelleecong/speecht5_finetuned_swahili") model = SpeechT5ForTextToSpeech.from_pretrained("samuelleecong/speecht5_finetuned_swahili").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): outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "translate"}) encoded_text = tokenizer(outputs["text"], return_tensors="pt") generated_tokens = model.generate(**encoded_text) output_translated = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) # return outputs["text"] return output_translated 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) 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: ![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()