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": [ ("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"), ("Northern Female Speaker", "ylacombe/vits_ljs_northern_female_monospeaker"), ("Midlands Male Speaker", "ylacombe/vits_ljs_midlands_male_monospeaker"), ("Southern Male Speaker", "ylacombe/vits_ljs_southern_male_monospeaker"), ("Irish Male Speaker", "ylacombe/vits_ljs_irish_male_monospeaker_2"), ], "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 = """# Explore English and Spanish Accents with VITS finetuning ## Or how the best wine comes in old bottles [VITS](https://huggingface.co/docs/transformers/model_doc/vits) is a light weight, low-latency TTS model. Coupled with the right data and the right training recipe, you can get an excellent finetuned version in **20 minutes** with as little as **80 to 150 samples**. Stay tuned, the training recipe is coming soon! """ 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[language]] 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 css = """ #container{ margin: 0 auto; max-width: 80rem; } #intro{ max-width: 100%; text-align: center; margin: 0 auto; } """ # Gradio blocks demo with gr.Blocks(css=css) as demo_blocks: gr.Markdown(title, elem_id="intro") 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" ) gr.Markdown(""" ## Datasets and models details ### English * **Model**: [VITS-ljs](https://huggingface.co/kakao-enterprise/vits-ljs) * **Dataset**: [British Isles Accent](https://huggingface.co/datasets/ylacombe/english_dialects). For each accent, we used 100 to 150 samples of a single speaker to finetune [VITS-ljs](https://huggingface.co/kakao-enterprise/vits-ljs). ### Spanish * **Model**: [Spanish MMS TTS](https://huggingface.co/facebook/mms-tts-spa). This model is part of Facebook's [Massively Multilingual Speech](https://arxiv.org/abs/2305.13516) project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html), and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts). * **Datasets**: For each accent, we used 100 to 150 samples of a single speaker to finetune the model. - [Colombian Spanish TTS dataset](https://huggingface.co/datasets/ylacombe/google-colombian-spanish). - [Argentinian Spanish TTS dataset](https://huggingface.co/datasets/ylacombe/google-argentinian-spanish). - [Chilean Spanish TTS dataset](https://huggingface.co/datasets/ylacombe/google-chilean-spanish). """) 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) with gr.Accordion("Run with transformers"): gr.Markdown( """## Running VITS and MMS with transformers ```bash pip install transformers ``` ```py from transformers import pipeline import scipy pipe = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs", device=0) results = pipe("A cinematic shot of a baby racoon wearing an intricate italian priest robe") # write to a wav file scipy.io.wavfile.write("audio_vits.wav", rate=results["sampling_rate"], data=results["audio"].squeeze()) ``` """ ) btn.click(generate_audio, [inp_text, language], outputs) demo_blocks.queue().launch()