File size: 3,229 Bytes
d347764 45f833a d347764 6927d82 d347764 6e5cca5 45f833a 6e5cca5 d347764 6e5cca5 d347764 6e5cca5 d347764 6e5cca5 d347764 f805e49 6e5cca5 c6f1d54 f805e49 c737803 d347764 226ec3a d347764 f805e49 d347764 c737803 3946ba6 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 81 82 83 |
import gradio as gr
import numpy as np
import torch
from datasets import load_dataset
from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor, pipeline, WhisperTokenizer
device = "cuda:0" if torch.cuda.is_available() else "cpu"
#tokenizer = WhisperTokenizer.from_pretrained("GatinhoEducado/whisper-tiny-finetuned-minds14", language="Portuguese", task="translate")
tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-tiny", language="Portuguese", task="translate")
# load speech translation checkpoint
asr_pipe = pipeline(
"automatic-speech-recognition",
model="GatinhoEducado/whisper-tiny-finetuned-minds14",
device=device,
tokenizer = tokenizer,
generate_kwargs = {"language":"<|pt|>",
"task": "transcribe",
"repetition_penalty":1.5
}
)
# load text-to-speech checkpoint and speaker embeddings
processor = SpeechT5Processor.from_pretrained("GatinhoEducado/speechT5_tts-finetuned-cml-tts")
model = SpeechT5ForTextToSpeech.from_pretrained("GatinhoEducado/speechT5_tts-finetuned-cml-tts").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"})
outputs = asr_pipe(audio, max_new_tokens=100)
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)
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 English to target Portuguese speech. 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(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()
|