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", } models_per_language = { "english": [ ("Irish Male Speaker", "ylacombe/vits_ljs_irish_male_monospeaker_2"), ("Welsh Female Speaker", "ylacombe/vits_ljs_welsh_female_monospeaker_2"), ("Welsh Male Speaker", "ylacombe/vits_ljs_welsh_male_monospeaker_2"), ("Scottish Female Speaker", "ylacombe/vits_ljs_scottish_female_monospeaker"), ], "spanish": [ ("Male Chilean Speaker", "ylacombe/mms-spa-finetuned-chilean-monospeaker"), ("Female Argentinian Speaker", "ylacombe/mms-spa-finetuned-argentinian-monospeaker"), ("Male Colombian Speaker", "ylacombe/mms-spa-finetuned-colombian-monospeaker"), ], } pipe_dict = { "pipe": [pipeline("text-to-speech", model=l[1], device=0) for l in models_per_language["english"]], "original_pipe": pipeline("text-to-speech", model=default_model_per_language["english"], device=0), "language": "english", } title = "# VITS" description = """ TODO """ max_speakers = 15 # Inference def generate_audio(text, language): if pipe_dict["language"] != language: gr.Warning(f"Language has changed - loading corresponding models: {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) pipe_dict["pipe"] = [pipeline("text-to-speech", model=l[1], device=0) for l in models_per_language["english"]] 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"Prediction from the original checkpoint {default_model_per_language[language]}", show_label=True, visible=True) out.append(output) for i in range(min(len(pipe_dict["pipe"]), max_speakers - 1)): output = pipe_dict["pipe"][i](text) output = gr.Audio(value = (output["sampling_rate"], output["audio"].squeeze()), type="numpy", autoplay=False, label=f"Finetuned {models_per_language[language][i][0]}", show_label=True, visible=True) out.append(output) out.extend([gr.Audio(visible=False)]*(max_speakers-(len(out)))) 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" ) 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) btn.click(generate_audio, [inp_text, language], outputs) demo_blocks.queue().launch()