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d347764 0636b54 d347764 8268044 d347764 8268044 d347764 16196d4 d347764 8268044 5967efe d347764 8268044 d347764 8268044 d347764 f805e49 8268044 c6f1d54 f805e49 c737803 73f1e96 c737803 728367a c737803 73f1e96 c737803 d347764 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | import gradio as gr
import numpy as np
import torch
from datasets import load_dataset
from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline
# hard coding CPU since the space runs on a CPU-only environment
device = torch.device("cpu")
# load speech translation checkpoint
asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
# load translation pipeline
translation_pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-nl", device=device)
# load text-to-speech checkpoint and speaker embeddings
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("sanchit-gandhi/speecht5_tts_vox_nl").to(device)
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan").to(device)
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", revision="ad29d262", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
def english_transcript(audio):
outputs = asr_pipe(audio, max_new_tokens=256)
return outputs["text"]
def translate_to_nl(text):
outputs = translation_pipe(text, max_new_tokens=256)
return outputs[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):
english_text = english_transcript(audio)
translated_text = translate_to_nl(english_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 English to Dutch. 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:
"""
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([file_translate], ["Audio File"])
demo.launch()
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