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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
from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
# from 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"})
m2m100_en_sw = pipeline('translation', 'facebook/m2m100_418M', src_lang='en', 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"})
output_translated = m2m100_en_sw(outputs["text"])
# 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[0]["translation_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)
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 Swahili. Demo uses OpenAI's [Whisper Base](https://huggingface.co/openai/whisper-base) model for speech transcription, Meta's [M2M100](https://huggingface.co/facebook/m2m100_418M) for translation, and Microsoft's
[SpeechT5 TTS](https://huggingface.co/samuelleecong/speecht5_finetuned_swahili) that I fine-tuned model for Swahili text-to-speech:
"""
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()
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