Upload app.py

app.py

@@ -0,0 +1,309 @@
+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()