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import gradio as gr
import numpy as np
import torch
from transformers import BarkModel
from transformers import AutoProcessor
from transformers import pipeline
import librosa
processor = AutoProcessor.from_pretrained("suno/bark-small")
model = BarkModel.from_pretrained("suno/bark-small")
device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c
language_presets = {"es":"v2/es_speaker_",
"en":"v2/en_speaker_"}
def tts(text, language="es", style:int = 0):
voice_preset = language_presets[language] + str(style)
# prepare the inputs
inputs = processor(text, voice_preset = voice_preset)
# generate speech
speech_output = model.generate(**inputs.to(device))
sampling_rate = model.generation_config.sample_rate
return speech_output[0].cpu().numpy(), sampling_rate
# load speech translation checkpoint
asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
def translate(audio, language:str = "es"):
outputs = asr_pipe(audio, max_new_tokens=256, generate_kwargs={"task": "transcribe", "language":language})
text = outputs["text"]
return text
def synthesise(text, language="es",style=0):
speech, sr = tts(text, language=language, style=style)
target_sr = 16_000
speech = librosa.resample(speech, orig_sr = sr, target_sr = target_sr)
return speech, target_sr
def speech_to_speech_translation(audio, debug = True):
translated_text = translate(audio)
if debug:
print(f"{translated_text=}")
synthesised_speech, sampling_rate = synthesise(translated_text)
# tranform to int for Gradio
synthesised_speech = (np.array(synthesised_speech) * 32767).astype(np.int16)
return sampling_rate, 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:
"""
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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