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| 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**. | |
| Training recipe available in this [github repository](https://github.com/ylacombe/finetune-hf-vits)! | |
| """ | |
| 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 sentence would you like 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) | |
| with gr.Accordion("Datasets and models details"): | |
| gr.Markdown(""" | |
| ### 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.Accordion("Run VITS and MMS with transformers", open=False): | |
| gr.Markdown( | |
| """ | |
| ```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() |