Bumping it to an increased version

Another version bump because I am new to twine

.github/dependabot.yml

@@ -7,6 +7,7 @@ version: 2
 updates:
   - package-ecosystem: "pip" # See documentation for possible values
     directory: "/" # Location of package manifests
+    target-branch: "dev"
     schedule:
       interval: "daily"
     ignore:

Makefile

@@ -17,7 +17,7 @@ create-package: ## Create wheel and tar gz
 	rm -rf dist/
 	python setup.py bdist_wheel --plat-name=manylinux1_x86_64
 	python setup.py sdist
-	python -m twine upload  dist/* --verbose
+	python -m twine upload  dist/* --verbose --skip-existing
 
 format: ## Run pre-commit hooks
 	pre-commit run -a

README.md

@@ -19,20 +19,23 @@
 
 > This is the official code implementation of 🍵 Matcha-TTS.
 
-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:
+We propose 🍵 Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses [conditional flow matching](https://arxiv.org/abs/2210.02747) (similar to [rectified flows](https://arxiv.org/abs/2209.03003)) 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).
+Check out our [demo page](https://shivammehta25.github.io/Matcha-TTS) and read [our arXiv preprint](https://arxiv.org/abs/2309.03199) for more details.
 
-[Pretrained models](https://drive.google.com/drive/folders/17C_gYgEHOxI5ZypcfE_k1piKCtyR0isJ?usp=sharing) will be auto downloaded with the CLI or gradio interface.
+[Pre-trained models](https://drive.google.com/drive/folders/17C_gYgEHOxI5ZypcfE_k1piKCtyR0isJ?usp=sharing) will be automatically downloaded with the CLI or gradio interface.
 
 [Try 🍵 Matcha-TTS on HuggingFace 🤗 spaces!](https://huggingface.co/spaces/shivammehta25/Matcha-TTS)
 
-<br>
+## Watch the teaser
+
+[![Watch the video](https://img.youtube.com/vi/xmvJkz3bqw0/hqdefault.jpg)](https://youtu.be/xmvJkz3bqw0)
+
 
 ## Installation
 
@@ -110,26 +113,13 @@ matcha-tts --text "<INPUT TEXT>" --temperature 0.667
 matcha-tts --text "<INPUT TEXT>" --steps 10
 ```
 
-## Citation information
-
-If you find this work useful, please cite our paper:
-
-```text
-@article{mehta2023matcha,
-  title={Matcha-TTS: A fast TTS architecture with conditional flow matching},
-  author={Mehta, Shivam and Tu, Ruibo and Beskow, Jonas and Sz{\'e}kely, {\'E}va and Henter, Gustav Eje},
-  journal={arXiv preprint arXiv:2309.03199},
-  year={2023}
-}
-```
-
 ## Train with your own dataset
 
-Let's assume we are training with LJSpeech
+Let's assume we are training with LJ Speech
 
-1. Download the dataset from [here](https://keithito.com/LJ-Speech-Dataset/), extract it to `data/LJSpeech-1.1`, and prepare the filelists to point to the extracted data like the [5th point of setup in Tacotron2 repo](https://github.com/NVIDIA/tacotron2#setup).
+1. Download the dataset from [here](https://keithito.com/LJ-Speech-Dataset/), extract it to `data/LJSpeech-1.1`, and prepare the file lists to point to the extracted data like for [item 5 in the setup of the NVIDIA Tacotron 2 repo](https://github.com/NVIDIA/tacotron2#setup).
 
-2. Clone and enter this repository
+2. Clone and enter the Matcha-TTS repository
 
 ```bash
 git clone https://github.com/shivammehta25/Matcha-TTS.git
@@ -167,7 +157,7 @@ data_statistics:  # Computed for ljspeech dataset
 
 to the paths of your train and validation filelists.
 
-5. Run the training script
+6. Run the training script
 
 ```bash
 make train-ljspeech
@@ -191,20 +181,33 @@ python matcha/train.py experiment=ljspeech_min_memory
 python matcha/train.py experiment=ljspeech trainer.devices=[0,1]
 ```
 
-6. Synthesise from the custom trained model
+7. Synthesise from the custom trained model
 
 ```bash
 matcha-tts --text "<INPUT TEXT>" --checkpoint_path <PATH TO CHECKPOINT>
 ```
 
+## Citation information
+
+If you use our code or otherwise find this work useful, please cite our paper:
+
+```text
+@article{mehta2023matcha,
+  title={Matcha-TTS: A fast TTS architecture with conditional flow matching},
+  author={Mehta, Shivam and Tu, Ruibo and Beskow, Jonas and Sz{\'e}kely, {\'E}va and Henter, Gustav Eje},
+  journal={arXiv preprint arXiv:2309.03199},
+  year={2023}
+}
+```
+
 ## Acknowledgements
 
-Since this code uses: [Lightning-Hydra-Template](https://github.com/ashleve/lightning-hydra-template), you have all the powers that comes with it.
+Since this code uses [Lightning-Hydra-Template](https://github.com/ashleve/lightning-hydra-template), you have all the powers that come with it.
 
-Other source codes I would like to acknowledge:
+Other source code I would like to acknowledge:
 
-- [Coqui-TTS](https://github.com/coqui-ai/TTS/tree/dev) :For helping me figure out how to make cython binaries pip installable and encouragement
+- [Coqui-TTS](https://github.com/coqui-ai/TTS/tree/dev): For helping me figure out how to make cython binaries pip installable and encouragement
 - [Hugging Face Diffusers](https://huggingface.co/): For their awesome diffusers library and its components
-- [Grad-TTS](https://github.com/huawei-noah/Speech-Backbones/tree/main/Grad-TTS): For source code of MAS
+- [Grad-TTS](https://github.com/huawei-noah/Speech-Backbones/tree/main/Grad-TTS): For the monotonic alignment search source code
 - [torchdyn](https://github.com/DiffEqML/torchdyn): Useful for trying other ODE solvers during research and development
-- [labml.ai](https://nn.labml.ai/transformers/rope/index.html): For RoPE implementation
+- [labml.ai](https://nn.labml.ai/transformers/rope/index.html): For the RoPE implementation

configs/debug/default.yaml

@@ -7,8 +7,8 @@
 task_name: "debug"
 
 # disable callbacks and loggers during debugging
-callbacks: null
+# callbacks: null
-logger: null
+# logger: null
 
 extras:
   ignore_warnings: False

configs/debug/profiler.yaml

@@ -7,6 +7,9 @@ defaults:
 
 trainer:
   max_epochs: 1
-  profiler: "simple"
+  # profiler: "simple"
-  # profiler: "advanced"
+  profiler: "advanced"
   # profiler: "pytorch"
+  accelerator: gpu
+
+  limit_train_batches: 0.02

matcha/VERSION

@@ -1 +1 @@
-0.0.1.dev4
+0.0.3

matcha/app.py

@@ -8,7 +8,7 @@ import torch
 
 from matcha.cli import (
     MATCHA_URLS,
-    VOCODER_URL,
+    VOCODER_URLS,
     assert_model_downloaded,
     get_device,
     load_matcha,
@@ -22,20 +22,73 @@ LOCATION = Path(get_user_data_dir())
 
 args = Namespace(
     cpu=False,
-    model="matcha_ljspeech",
+    model="matcha_vctk",
-    vocoder="hifigan_T2_v1",
+    vocoder="hifigan_univ_v1",
-    spk=None,
+    spk=0,
 )
 
-MATCHA_TTS_LOC = LOCATION / f"{args.model}.ckpt"
+CURRENTLY_LOADED_MODEL = args.model
-VOCODER_LOC = LOCATION / f"{args.vocoder}"
+
+MATCHA_TTS_LOC = lambda x: LOCATION / f"{x}.ckpt"  # noqa: E731
+VOCODER_LOC = lambda x: LOCATION / f"{x}"  # noqa: E731
 LOGO_URL = "https://shivammehta25.github.io/Matcha-TTS/images/logo.png"
-assert_model_downloaded(MATCHA_TTS_LOC, MATCHA_URLS[args.model])
+RADIO_OPTIONS = {
-assert_model_downloaded(VOCODER_LOC, VOCODER_URL[args.vocoder])
+    "Multi Speaker (VCTK)": {
+        "model": "matcha_vctk",
+        "vocoder": "hifigan_univ_v1",
+    },
+    "Single Speaker (LJ Speech)": {
+        "model": "matcha_ljspeech",
+        "vocoder": "hifigan_T2_v1",
+    },
+}
+
+# Ensure all the required models are downloaded
+assert_model_downloaded(MATCHA_TTS_LOC("matcha_ljspeech"), MATCHA_URLS["matcha_ljspeech"])
+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)
 
-model = load_matcha(args.model, MATCHA_TTS_LOC, device)
+# Load default model
-vocoder, denoiser = load_vocoder(args.vocoder, VOCODER_LOC, device)
+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 == "matcha_ljspeech":
+        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()
@@ -45,13 +98,14 @@ def process_text_gradio(text):
 
 
 @torch.inference_mode()
-def synthesise_mel(text, text_length, n_timesteps, temperature, length_scale):
+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=args.spk,
+        spks=spk,
         length_scale=length_scale,
     )
     output["waveform"] = to_waveform(output["mel"], vocoder, denoiser)
@@ -61,9 +115,27 @@ def synthesise_mel(text, text_length, n_timesteps, temperature, length_scale):
     return fp.name, plot_tensor(output["mel"].squeeze().cpu().numpy())
 
 
-def run_full_synthesis(text, n_timesteps, mel_temp, length_scale):
+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)
+    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 != "matcha_ljspeech":
+        global model, vocoder, denoiser  # pylint: disable=global-statement
+        model, vocoder, denoiser = load_model("matcha_ljspeech", "hifigan_T2_v1")
+        CURRENTLY_LOADED_MODEL = "matcha_ljspeech"
+
+    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
 
 
@@ -92,20 +164,31 @@ def main():
         with gr.Box():
             with gr.Row():
                 gr.Markdown(description, scale=3)
-                gr.Image(LOGO_URL, label="Matcha-TTS logo", height=150, width=150, scale=1, show_label=False)
+                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")
+                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=0,
+                    minimum=1,
                     maximum=100,
                     step=1,
                     value=10,
@@ -142,58 +225,110 @@ def main():
                 # with gr.Row():
                 audio = gr.Audio(interactive=False, label="Audio")
 
-        with gr.Row():
+        with gr.Row(visible=False) as example_row_lj_speech:
             examples = gr.Examples(  # pylint: disable=unused-variable
                 examples=[
                     [
                         "We propose Matcha-TTS, a new approach to non-autoregressive neural TTS, that uses conditional flow matching (similar to rectified flows) to speed up O D E-based speech synthesis.",
                         50,
                         0.677,
-                        1.0,
+                        0.95,
                     ],
                     [
                         "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.",
                         2,
                         0.677,
-                        1.0,
+                        0.95,
                     ],
                     [
                         "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.",
                         4,
                         0.677,
-                        1.0,
+                        0.95,
                     ],
                     [
                         "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.",
                         10,
                         0.677,
-                        1.0,
+                        0.95,
                     ],
                     [
                         "The Secret Service believed that it was very doubtful that any President would ride regularly in a vehicle with a fixed top, even though transparent.",
                         50,
                         0.677,
-                        1.0,
+                        0.95,
                     ],
                     [
                         "The narrative of these events is based largely on the recollections of the participants.",
                         10,
                         0.677,
-                        1.0,
+                        0.95,
                     ],
                     [
                         "The jury did not believe him, and the verdict was for the defendants.",
                         10,
                         0.677,
-                        1.0,
+                        0.95,
                     ],
                 ],
-                fn=run_full_synthesis,
+                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,
+                    ],
+                    [
+                        "Hello everyone! I am speaker 44 and I am here to tell you that Matcha-TTS is amazing!",
+                        50,
+                        0.677,
+                        0.85,
+                        44,
+                    ],
+                    [
+                        "Hello everyone! I am speaker 45 and I am here to tell you that Matcha-TTS is amazing!",
+                        50,
+                        0.677,
+                        0.85,
+                        45,
+                    ],
+                    [
+                        "Hello everyone! I am speaker 58 and I am here to tell you that Matcha-TTS is amazing!",
+                        4,
+                        0.677,
+                        0.85,
+                        58,
+                    ],
+                ],
+                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=[
@@ -204,11 +339,11 @@ def main():
             queue=True,
         ).then(
             fn=synthesise_mel,
-            inputs=[processed_text, processed_text_len, n_timesteps, mel_temp, length_scale],
+            inputs=[processed_text, processed_text_len, n_timesteps, mel_temp, length_scale, spk_slider],
             outputs=[audio, mel_spectrogram],
         )
 
-        demo.queue(concurrency_count=5).launch(share=True)
+        demo.queue().launch(share=True)
 
 
 if __name__ == "__main__":

matcha/cli.py

@@ -1,6 +1,7 @@
 import argparse
 import datetime as dt
 import os
+import warnings
 from pathlib import Path
 
 import matplotlib.pyplot as plt
@@ -17,13 +18,20 @@ from matcha.text import sequence_to_text, text_to_sequence
 from matcha.utils.utils import assert_model_downloaded, get_user_data_dir, intersperse
 
 MATCHA_URLS = {
-    "matcha_ljspeech": "https://drive.google.com/file/d/1BBzmMU7k3a_WetDfaFblMoN18GqQeHCg/view?usp=drive_link"
+    "matcha_ljspeech": "https://drive.google.com/file/d/1BBzmMU7k3a_WetDfaFblMoN18GqQeHCg/view?usp=drive_link",
-}  # , "matcha_vctk": ""}  # Coming soon
+    "matcha_vctk": "https://drive.google.com/file/d/1enuxmfslZciWGAl63WGh2ekVo00FYuQ9/view?usp=drive_link",
+}
 
-MULTISPEAKER_MODEL = {"matcha_vctk"}
+VOCODER_URLS = {
-SINGLESPEAKER_MODEL = {"matcha_ljspeech"}
+    "hifigan_T2_v1": "https://drive.google.com/file/d/14NENd4equCBLyyCSke114Mv6YR_j_uFs/view?usp=drive_link",
+    "hifigan_univ_v1": "https://drive.google.com/file/d/1qpgI41wNXFcH-iKq1Y42JlBC9j0je8PW/view?usp=drive_link",
+}
 
-VOCODER_URL = {"hifigan_T2_v1": "https://drive.google.com/file/d/14NENd4equCBLyyCSke114Mv6YR_j_uFs/view?usp=drive_link"}
+MULTISPEAKER_MODEL = {
+    "matcha_vctk": {"vocoder": "hifigan_univ_v1", "speaking_rate": 0.85, "spk": 0, "spk_range": (0, 107)}
+}
+
+SINGLESPEAKER_MODEL = {"matcha_ljspeech": {"vocoder": "hifigan_T2_v1", "speaking_rate": 0.95, "spk": None}}
 
 
 def plot_spectrogram_to_numpy(spectrogram, filename):
@@ -62,10 +70,14 @@ def get_texts(args):
 
 def assert_required_models_available(args):
     save_dir = get_user_data_dir()
+    if not hasattr(args, "checkpoint_path") and args.checkpoint_path is None:
+        model_path = args.checkpoint_path
+    else:
         model_path = save_dir / f"{args.model}.ckpt"
-    vocoder_path = save_dir / f"{args.vocoder}"
         assert_model_downloaded(model_path, MATCHA_URLS[args.model])
-    assert_model_downloaded(vocoder_path, VOCODER_URL[args.vocoder])
+
+    vocoder_path = save_dir / f"{args.vocoder}"
+    assert_model_downloaded(vocoder_path, VOCODER_URLS[args.vocoder])
     return {"matcha": model_path, "vocoder": vocoder_path}
 
 
@@ -81,7 +93,7 @@ def load_hifigan(checkpoint_path, device):
 def load_vocoder(vocoder_name, checkpoint_path, device):
     print(f"[!] Loading {vocoder_name}!")
     vocoder = None
-    if vocoder_name == "hifigan_T2_v1":
+    if vocoder_name in ("hifigan_T2_v1", "hifigan_univ_v1"):
         vocoder = load_hifigan(checkpoint_path, device)
     else:
         raise NotImplementedError(
@@ -124,21 +136,70 @@ def validate_args(args):
         args.text or args.file
     ), "Either text or file must be provided Matcha-T(ea)TTS need sometext to whisk the waveforms."
     assert args.temperature >= 0, "Sampling temperature cannot be negative"
-    assert args.speaking_rate > 0, "Speaking rate must be greater than 0"
     assert args.steps > 0, "Number of ODE steps must be greater than 0"
-    if args.model in SINGLESPEAKER_MODEL:
+
-        assert args.spk is None, f"Speaker ID is not supported for {args.model}"
+    if args.checkpoint_path is None:
-    if args.spk is not None:
+        # When using pretrained models
-        assert args.spk >= 0 and args.spk < 109, "Speaker ID must be between 0 and 108"
+        if args.model in SINGLESPEAKER_MODEL.keys():
-        assert args.model in MULTISPEAKER_MODEL, "Speaker ID is only supported for multispeaker model"
+            args = validate_args_for_single_speaker_model(args)
 
         if args.model in MULTISPEAKER_MODEL:
-        if args.spk is None:
+            args = validate_args_for_multispeaker_model(args)
-            print("[!] Speaker ID not provided! Using speaker ID 0")
+    else:
-            args.spk = 0
+        # When using a custom model
+        if args.vocoder != "hifigan_univ_v1":
+            warn_ = "[-] Using custom model checkpoint! I would suggest passing --vocoder hifigan_univ_v1, unless the custom model is trained on LJ Speech."
+            warnings.warn(warn_, UserWarning)
+        if args.speaking_rate is None:
+            args.speaking_rate = 1.0
 
     if args.batched:
         assert args.batch_size > 0, "Batch size must be greater than 0"
+    assert args.speaking_rate > 0, "Speaking rate must be greater than 0"
+
+    return args
+
+
+def validate_args_for_multispeaker_model(args):
+    if args.vocoder is not None:
+        if args.vocoder != MULTISPEAKER_MODEL[args.model]["vocoder"]:
+            warn_ = f"[-] Using {args.model} model! I would suggest passing --vocoder {MULTISPEAKER_MODEL[args.model]['vocoder']}"
+            warnings.warn(warn_, UserWarning)
+    else:
+        args.vocoder = MULTISPEAKER_MODEL[args.model]["vocoder"]
+
+    if args.speaking_rate is None:
+        args.speaking_rate = MULTISPEAKER_MODEL[args.model]["speaking_rate"]
+
+    spk_range = MULTISPEAKER_MODEL[args.model]["spk_range"]
+    if args.spk is not None:
+        assert (
+            args.spk >= spk_range[0] and args.spk <= spk_range[-1]
+        ), f"Speaker ID must be between {spk_range} for this model."
+    else:
+        available_spk_id = MULTISPEAKER_MODEL[args.model]["spk"]
+        warn_ = f"[!] Speaker ID not provided! Using speaker ID {available_spk_id}"
+        warnings.warn(warn_, UserWarning)
+        args.spk = available_spk_id
+
+    return args
+
+
+def validate_args_for_single_speaker_model(args):
+    if args.vocoder is not None:
+        if args.vocoder != SINGLESPEAKER_MODEL[args.model]["vocoder"]:
+            warn_ = f"[-] Using {args.model} model! I would suggest passing --vocoder {SINGLESPEAKER_MODEL[args.model]['vocoder']}"
+            warnings.warn(warn_, UserWarning)
+    else:
+        args.vocoder = SINGLESPEAKER_MODEL[args.model]["vocoder"]
+
+    if args.speaking_rate is None:
+        args.speaking_rate = SINGLESPEAKER_MODEL[args.model]["speaking_rate"]
+
+    if args.spk != SINGLESPEAKER_MODEL[args.model]["spk"]:
+        warn_ = f"[-] Ignoring speaker id {args.spk} for {args.model}"
+        warnings.warn(warn_, UserWarning)
+        args.spk = SINGLESPEAKER_MODEL[args.model]["spk"]
 
     return args
 
@@ -166,9 +227,9 @@ def cli():
     parser.add_argument(
         "--vocoder",
         type=str,
-        default="hifigan_T2_v1",
+        default=None,
-        help="Vocoder to use",
+        help="Vocoder to use (default: will use the one suggested with the pretrained model))",
-        choices=VOCODER_URL.keys(),
+        choices=VOCODER_URLS.keys(),
     )
     parser.add_argument("--text", type=str, default=None, help="Text to synthesize")
     parser.add_argument("--file", type=str, default=None, help="Text file to synthesize")
@@ -182,7 +243,7 @@ def cli():
     parser.add_argument(
         "--speaking_rate",
         type=float,
-        default=1.0,
+        default=None,
         help="change the speaking rate, a higher value means slower speaking rate (default: 1.0)",
     )
     parser.add_argument("--steps", type=int, default=10, help="Number of ODE steps  (default: 10)")
@@ -199,8 +260,10 @@ def cli():
         default=os.getcwd(),
         help="Output folder to save results (default: current dir)",
     )
-    parser.add_argument("--batched", action="store_true")
+    parser.add_argument("--batched", action="store_true", help="Batched inference (default: False)")
-    parser.add_argument("--batch_size", type=int, default=32)
+    parser.add_argument(
+        "--batch_size", type=int, default=32, help="Batch size only useful when --batched (default: 32)"
+    )
 
     args = parser.parse_args()
 
@@ -333,6 +396,8 @@ def unbatched_synthesis(args, device, model, vocoder, denoiser, texts, spk):
 
 def print_config(args):
     print("[!] Configurations: ")
+    print(f"\t- Model: {args.model}")
+    print(f"\t- Vocoder: {args.vocoder}")
     print(f"\t- Temperature: {args.temperature}")
     print(f"\t- Speaking rate: {args.speaking_rate}")
     print(f"\t- Number of ODE steps: {args.steps}")

requirements.txt

@@ -35,7 +35,7 @@ torchaudio
 matplotlib
 pandas
 conformer==0.3.2
-diffusers==0.21.1
+diffusers==0.21.2
 notebook
 ipywidgets
 gradio