app.py

import torch

from transformers import pipeline

import numpy as np
import gradio as gr

def _grab_best_device(use_gpu=True):
 if torch.cuda.device_count() > 0 and use_gpu:
 device = "cuda"
 else:
 device = "cpu"
 return device

device = _grab_best_device()

default_model_per_language = {
 "english": "kakao-enterprise/vits-ljs",
 "spanish": "facebook/mms-tts-spa",
 "tamil": "facebook/mms-tts-tam",
 "gujarati": "facebook/mms-tts-guj",
 "marathi": "facebook/mms-tts-mar"
}

models_per_language = {
 "english": [
 "ylacombe/vits_ljs_irish_male_monospeaker_2",
 
 "ylacombe/vits_ljs_irish_male_monospeaker_2",

 "ylacombe/vits_ljs_welsh_female_monospeaker_2",

 "ylacombe/vits_ljs_welsh_male_monospeaker_2",

 "ylacombe/vits_ljs_scottish_female_monospeaker",
 ],
 "spanish": [
 "ylacombe/mms-spa-finetuned-chilean-monospeaker", 
 ],
 "tamil": [
 "ylacombe/mms-tam-finetuned-monospeaker",
 ],
 "gujarati" : ["ylacombe/mms-guj-finetuned-monospeaker"],
 "marathi": ["ylacombe/mms-mar-finetuned-monospeaker"]
}

HUB_PATH = "ylacombe/vits_ljs_welsh_female_monospeaker_2"


pipe_dict = {
 "current_model": "ylacombe/vits_ljs_welsh_female_monospeaker_2",
 "pipe": pipeline("text-to-speech", model=HUB_PATH, device=0),
 "original_pipe": pipeline("text-to-speech", model=default_model_per_language["english"], device=0),
 "language": "english",
}

title = "# 🐶 VITS"

max_speakers = 15

description = """

"""


# Inference
def generate_audio(text, model_id, language):

 if pipe_dict["language"] != language:
 gr.Warning(f"Language has changed - loading new default model: {default_model_per_language[language]}")
 pipe_dict["language"] = language
 pipe_dict["original_pipe"] = pipeline("text-to-speech", model=default_model_per_language[language], device=0)
 
 if pipe_dict["current_model"] != model_id:
 gr.Warning("Model has changed - loading new model")
 pipe_dict["pipe"] = pipeline("text-to-speech", model=model_id, device=0)
 pipe_dict["current_model"] = model_id

 num_speakers = pipe_dict["pipe"].model.config.num_speakers

 out = []
 # first generate original model result
 output = pipe_dict["original_pipe"](text)
 output = gr.Audio(value = (output["sampling_rate"], output["audio"].squeeze()), type="numpy", autoplay=False, label=f"Non finetuned model prediction {default_model_per_language[language]}", show_label=True,
 visible=True)
 out.append(output)
 
 
 if num_speakers>1:
 for i in range(min(num_speakers, max_speakers - 1)):
 forward_params = {"speaker_id": i}
 output = pipe_dict["pipe"](text, forward_params=forward_params)
 
 output = gr.Audio(value = (output["sampling_rate"], output["audio"].squeeze()), type="numpy", autoplay=False, label=f"Generated Audio - speaker {i}", show_label=True,
 visible=True)
 out.append(output)
 out.extend([gr.Audio(visible=False)]*(max_speakers-num_speakers))
 else:
 output = pipe_dict["pipe"](text)
 output = gr.Audio(value = (output["sampling_rate"], output["audio"].squeeze()), type="numpy", autoplay=False, label="Generated Audio - Mono speaker", show_label=True,
 visible=True)
 out.append(output)
 out.extend([gr.Audio(visible=False)]*(max_speakers-2))
 return out


# Gradio blocks demo 
with gr.Blocks() as demo_blocks:
 gr.Markdown(title)
 gr.Markdown(description)
 with gr.Row():
 with gr.Column():
 inp_text = gr.Textbox(label="Input Text", info="What would you like VITS to synthesise?")
 btn = gr.Button("Generate Audio!")
 language = gr.Dropdown(
 default_model_per_language.keys(),
 value = "english",
 label = "language",
 info = "Language that you want to test"
 )
 
 model_id = gr.Dropdown(
 models_per_language["english"],
 value="ylacombe/vits_ljs_welsh_female_monospeaker_2", 
 label="Model", 
 info="Model you want to test",
 )
 
 with gr.Column():
 outputs = []
 for i in range(max_speakers):
 out_audio = gr.Audio(type="numpy", autoplay=False, label=f"Generated Audio - speaker {i}", show_label=True, visible=False)
 outputs.append(out_audio)

 language.change(lambda language: gr.Dropdown(
 models_per_language[language],
 value=models_per_language[language][0], 
 label="Model", 
 info="Model you want to test",
 ),
 language,
 model_id
 )
 
 btn.click(generate_audio, [inp_text, model_id, language], outputs)
 

demo_blocks.queue().launch()