Delete matcha/app.py

matcha/app.py

@@ -1,309 +0,0 @@
-import tempfile
-from argparse import Namespace
-from pathlib import Path
-
-import gradio as gr
-import soundfile as sf
-import torch
-
-from matcha.cli import (
-    MATCHA_URLS,
-    VOCODER_URLS,
-    assert_model_downloaded,
-    get_device,
-    load_matcha,
-    load_vocoder,
-    process_text,
-    to_waveform,
-)
-from matcha.utils.utils import get_user_data_dir, plot_tensor
-
-LOCATION = Path(get_user_data_dir())
-
-args = Namespace(
-    cpu=True,
-    model="akyl_ai",
-    vocoder="hifigan_T2_v1",
-    spk=0,
-)
-
-CURRENTLY_LOADED_MODEL = args.model
-
-
-def MATCHA_TTS_LOC(x):
-    return LOCATION / f"{x}.ckpt"
-
-
-def VOCODER_LOC(x):
-    return LOCATION / f"{x}"
-
-
-LOGO_URL = "https://shivammehta25.github.io/Matcha-TTS/images/logo.png"
-RADIO_OPTIONS = {
-    "Multi Speaker (VCTK)": {
-        "model": "matcha_vctk",
-        "vocoder": "hifigan_univ_v1",
-    },
-    "Single Speaker (LJ Speech)": {
-        "model": "akyl_ai",
-        "vocoder": "hifigan_T2_v1",
-    },
-}
-
-# Ensure all the required models are downloaded
-assert_model_downloaded(MATCHA_TTS_LOC("akyl_ai"), MATCHA_URLS["akyl_ai"])
-assert_model_downloaded(VOCODER_LOC("hifigan_T2_v1"), VOCODER_URLS["hifigan_T2_v1"])
-assert_model_downloaded(MATCHA_TTS_LOC("matcha_vctk"), MATCHA_URLS["matcha_vctk"])
-assert_model_downloaded(VOCODER_LOC("hifigan_univ_v1"), VOCODER_URLS["hifigan_univ_v1"])
-
-device = get_device(args)
-
-# Load default model
-model = load_matcha(args.model, MATCHA_TTS_LOC(args.model), device)
-vocoder, denoiser = load_vocoder(args.vocoder, VOCODER_LOC(args.vocoder), device)
-
-
-def load_model(model_name, vocoder_name):
-    model = load_matcha(model_name, MATCHA_TTS_LOC(model_name), device)
-    vocoder, denoiser = load_vocoder(vocoder_name, VOCODER_LOC(vocoder_name), device)
-    return model, vocoder, denoiser
-
-
-def load_model_ui(model_type, textbox):
-    model_name, vocoder_name = RADIO_OPTIONS[model_type]["model"], RADIO_OPTIONS[model_type]["vocoder"]
-
-    global model, vocoder, denoiser, CURRENTLY_LOADED_MODEL  # pylint: disable=global-statement
-    if CURRENTLY_LOADED_MODEL != model_name:
-        model, vocoder, denoiser = load_model(model_name, vocoder_name)
-        CURRENTLY_LOADED_MODEL = model_name
-
-    if model_name == "akyl_ai":
-        spk_slider = gr.update(visible=False, value=-1)
-        single_speaker_examples = gr.update(visible=True)
-        multi_speaker_examples = gr.update(visible=False)
-        length_scale = gr.update(value=0.95)
-    else:
-        spk_slider = gr.update(visible=True, value=0)
-        single_speaker_examples = gr.update(visible=False)
-        multi_speaker_examples = gr.update(visible=True)
-        length_scale = gr.update(value=0.85)
-
-    return (
-        textbox,
-        gr.update(interactive=True),
-        spk_slider,
-        single_speaker_examples,
-        multi_speaker_examples,
-        length_scale,
-    )
-
-
-@torch.inference_mode()
-def process_text_gradio(text):
-    output = process_text(1, text, device)
-    return output["x_phones"][1::2], output["x"], output["x_lengths"]
-
-
-@torch.inference_mode()
-def synthesise_mel(text, text_length, n_timesteps, temperature, length_scale, spk):
-    spk = torch.tensor([spk], device=device, dtype=torch.long) if spk >= 0 else None
-    output = model.synthesise(
-        text,
-        text_length,
-        n_timesteps=n_timesteps,
-        temperature=temperature,
-        spks=spk,
-        length_scale=length_scale,
-    )
-    output["waveform"] = to_waveform(output["mel"], vocoder, denoiser)
-    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
-        sf.write(fp.name, output["waveform"], 22050, "PCM_24")
-
-    return fp.name, plot_tensor(output["mel"].squeeze().cpu().numpy())
-
-
-def multispeaker_example_cacher(text, n_timesteps, mel_temp, length_scale, spk):
-    global CURRENTLY_LOADED_MODEL  # pylint: disable=global-statement
-    if CURRENTLY_LOADED_MODEL != "matcha_vctk":
-        global model, vocoder, denoiser  # pylint: disable=global-statement
-        model, vocoder, denoiser = load_model("matcha_vctk", "hifigan_univ_v1")
-        CURRENTLY_LOADED_MODEL = "matcha_vctk"
-
-    phones, text, text_lengths = process_text_gradio(text)
-    audio, mel_spectrogram = synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk)
-    return phones, audio, mel_spectrogram
-
-
-def ljspeech_example_cacher(text, n_timesteps, mel_temp, length_scale, spk=-1):
-    global CURRENTLY_LOADED_MODEL  # pylint: disable=global-statement
-    if CURRENTLY_LOADED_MODEL != "akyl_ai":
-        global model, vocoder, denoiser  # pylint: disable=global-statement
-        model, vocoder, denoiser = load_model("akyl_ai", "hifigan_T2_v1")
-        CURRENTLY_LOADED_MODEL = "akyl_ai"
-
-    phones, text, text_lengths = process_text_gradio(text)
-    audio, mel_spectrogram = synthesise_mel(text, text_lengths, n_timesteps, mel_temp, length_scale, spk)
-    return phones, audio, mel_spectrogram
-
-
-def main():
-    description = """# 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching
-    ### [Shivam Mehta](https://www.kth.se/profile/smehta), [Ruibo Tu](https://www.kth.se/profile/ruibo), [Jonas Beskow](https://www.kth.se/profile/beskow), [Éva Székely](https://www.kth.se/profile/szekely), and [Gustav Eje Henter](https://people.kth.se/~ghe/)
-    We propose 🍵 Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses conditional flow matching (similar to rectified flows) to speed up ODE-based speech synthesis. Our method:
-
-
-    * Is probabilistic
-    * Has compact memory footprint
-    * Sounds highly natural
-    * Is very fast to synthesise from
-
-
-    Check out our [demo page](https://shivammehta25.github.io/Matcha-TTS). Read our [arXiv preprint for more details](https://arxiv.org/abs/2309.03199).
-    Code is available in our [GitHub repository](https://github.com/shivammehta25/Matcha-TTS), along with pre-trained models.
-
-    Cached examples are available at the bottom of the page.
-    """
-
-    with gr.Blocks(title="🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching") as demo:
-        processed_text = gr.State(value=None)
-        processed_text_len = gr.State(value=None)
-
-        with gr.Box():
-            with gr.Row():
-                gr.Markdown(description, scale=3)
-                with gr.Column():
-                    gr.Image(LOGO_URL, label="Matcha-TTS logo", height=50, width=50, scale=1, show_label=False)
-                    html = '<br><iframe width="560" height="315" src="https://www.youtube.com/embed/xmvJkz3bqw0?si=jN7ILyDsbPwJCGoa" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>'
-                    gr.HTML(html)
-
-        with gr.Box():
-            radio_options = list(RADIO_OPTIONS.keys())
-            model_type = gr.Radio(
-                radio_options, value=radio_options[0], label="Choose a Model", interactive=True, container=False
-            )
-
-            with gr.Row():
-                gr.Markdown("# Text Input")
-            with gr.Row():
-                text = gr.Textbox(value="", lines=2, label="Text to synthesise", scale=3)
-                spk_slider = gr.Slider(
-                    minimum=0, maximum=107, step=1, value=args.spk, label="Speaker ID", interactive=True, scale=1
-                )
-
-            with gr.Row():
-                gr.Markdown("### Hyper parameters")
-            with gr.Row():
-                n_timesteps = gr.Slider(
-                    label="Number of ODE steps",
-                    minimum=1,
-                    maximum=100,
-                    step=1,
-                    value=10,
-                    interactive=True,
-                )
-                length_scale = gr.Slider(
-                    label="Length scale (Speaking rate)",
-                    minimum=0.5,
-                    maximum=1.5,
-                    step=0.05,
-                    value=1.0,
-                    interactive=True,
-                )
-                mel_temp = gr.Slider(
-                    label="Sampling temperature",
-                    minimum=0.00,
-                    maximum=2.001,
-                    step=0.16675,
-                    value=0.667,
-                    interactive=True,
-                )
-
-                synth_btn = gr.Button("Synthesise")
-
-        with gr.Box():
-            with gr.Row():
-                gr.Markdown("### Phonetised text")
-                phonetised_text = gr.Textbox(interactive=False, scale=10, label="Phonetised text")
-
-        with gr.Box():
-            with gr.Row():
-                mel_spectrogram = gr.Image(interactive=False, label="mel spectrogram")
-
-                # with gr.Row():
-                audio = gr.Audio(interactive=False, label="Audio")
-
-        with gr.Row(visible=False) as example_row_lj_speech:
-            examples = gr.Examples(  # pylint: disable=unused-variable
-                examples=[
-                    [
-                        "Баарыңарга салам, менин атым Акылай. Мен бардыгын бул жерде Инновация борборунда көргөнүмө абдан кубанычтамын.",
-                        50,
-                        0.677,
-                        0.95,
-                    ],
-                    [
-                        "Мага колдоо көрсөтүп, мени тандагандарга ыраазымын. Айыл үчүн иштейбиз, жол курабыз, асфальт төшөйбүз”, — деген ал.",
-                        2,
-                        0.677,
-                        0.95,
-                    ],
- 
-                  
-                ],
-                fn=ljspeech_example_cacher,
-                inputs=[text, n_timesteps, mel_temp, length_scale],
-                outputs=[phonetised_text, audio, mel_spectrogram],
-                cache_examples=True,
-            )
-
-        with gr.Row() as example_row_multispeaker:
-            multi_speaker_examples = gr.Examples(  # pylint: disable=unused-variable
-                examples=[
-                    [
-                        "Hello everyone! I am speaker 0 and I am here to tell you that Matcha-TTS is amazing!",
-                        10,
-                        0.677,
-                        0.85,
-                        0,
-                    ],
-                    [
-                        "Hello everyone! I am speaker 16 and I am here to tell you that Matcha-TTS is amazing!",
-                        10,
-                        0.677,
-                        0.85,
-                        16,
-                    ],
-                    
-                ],
-                fn=multispeaker_example_cacher,
-                inputs=[text, n_timesteps, mel_temp, length_scale, spk_slider],
-                outputs=[phonetised_text, audio, mel_spectrogram],
-                cache_examples=True,
-                label="Multi Speaker Examples",
-            )
-
-        model_type.change(lambda x: gr.update(interactive=False), inputs=[synth_btn], outputs=[synth_btn]).then(
-            load_model_ui,
-            inputs=[model_type, text],
-            outputs=[text, synth_btn, spk_slider, example_row_lj_speech, example_row_multispeaker, length_scale],
-        )
-
-        synth_btn.click(
-            fn=process_text_gradio,
-            inputs=[
-                text,
-            ],
-            outputs=[phonetised_text, processed_text, processed_text_len],
-            api_name="matcha_tts",
-            queue=True,
-        ).then(
-            fn=synthesise_mel,
-            inputs=[processed_text, processed_text_len, n_timesteps, mel_temp, length_scale, spk_slider],
-            outputs=[audio, mel_spectrogram],
-        )
-
-        demo.queue().launch(share=True)
-
-
-if __name__ == "__main__":
-    main()