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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
speaker_embedding_path = "./speaker_embedding.npy"
replacements = [
("&", "og"),
("\r", " "),
("´", ""),
("\\", ""),
("¨", " "),
("Å", "AA"),
("Æ", "AE"),
("É", "E"),
("Ö", "OE"),
("Ø", "OE"),
("á", "a"),
("ä", "ae"),
("å", "aa"),
("è", "e"),
("î", "i"),
("ô", "oe"),
("ö", "oe"),
("ø", "oe"),
("ü", "y"),
]
def replace_danish_letters(text):
for src, dst in replacements:
text = text.replace(src, dst)
return text
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("JackismyShephard/speecht5_tts-finetuned-nst-da")
model = SpeechT5ForTextToSpeech.from_pretrained("JackismyShephard/speecht5_tts-finetuned-nst-da").to(device)
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
speaker_embedding = np.load(speaker_embedding_path)
speaker_embeddings = torch.tensor(speaker_embedding).unsqueeze(0)
def translate(audio):
outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language": "da"})
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)
translated_text = replace_danish_letters(translated_text)
print(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 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 fine-tuned by [JackismyShephard](https://huggingface.co/JackismyShephard) for Danish for text-to-speech:
"""
demo = gr.Blocks()
mic_translate = gr.Interface(
fn=speech_to_speech_translation,
inputs=gr.Audio(sources=["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(sources=["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() |