Bump diffusers from 0.25.0 to 0.27.2

Bumps [diffusers](https://github.com/huggingface/diffusers) from 0.25.0 to 0.27.2.
- [Release notes](https://github.com/huggingface/diffusers/releases)
- [Commits](https://github.com/huggingface/diffusers/compare/v0.25.0...v0.27.2)

---
updated-dependencies:
- dependency-name: diffusers
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

.pre-commit-config.yaml

@@ -1,9 +1,9 @@
 default_language_version:
-  python: python3.11
+  python: python3.10
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v4.4.0
     hooks:
       # list of supported hooks: https://pre-commit.com/hooks.html
       - id: trailing-whitespace
@@ -18,28 +18,28 @@ repos:
 
   # python code formatting
   - repo: https://github.com/psf/black
-    rev: 23.12.1
+    rev: 23.9.1
     hooks:
       - id: black
         args: [--line-length, "120"]
 
   # python import sorting
   - repo: https://github.com/PyCQA/isort
-    rev: 5.13.2
+    rev: 5.12.0
     hooks:
       - id: isort
         args: ["--profile", "black", "--filter-files"]
 
   # python upgrading syntax to newer version
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.15.0
+    rev: v3.14.0
     hooks:
       - id: pyupgrade
         args: [--py38-plus]
 
   # python check (PEP8), programming errors and code complexity
   - repo: https://github.com/PyCQA/flake8
-    rev: 7.0.0
+    rev: 6.1.0
     hooks:
       - id: flake8
         args:
@@ -54,6 +54,6 @@ repos:
 
   # pylint
   - repo: https://github.com/pycqa/pylint
-    rev: v3.0.3
+    rev: v3.0.0
     hooks:
     -   id: pylint

README.md

@@ -17,7 +17,7 @@
 
 </div>
 
-> This is the official code implementation of 🍵 Matcha-TTS [ICASSP 2024].
+> 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](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:
 
@@ -252,43 +252,6 @@ python3 -m matcha.onnx.infer model.onnx --text "hey" --output-dir ./outputs --vo
 
 This will write `.wav` audio files to the output directory.
 
-## Extract phoneme alignments from Matcha-TTS
-
-If the dataset is structured as
-
-```bash
-data/
-└── LJSpeech-1.1
-    ├── metadata.csv
-    ├── README
-    ├── test.txt
-    ├── train.txt
-    ├── val.txt
-    └── wavs
-```
-Then you can extract the phoneme level alignments from a Trained Matcha-TTS model using:
-```bash
-python  matcha/utils/get_durations_from_trained_model.py -i dataset_yaml -c <checkpoint>
-```
-Example:
-```bash
-python  matcha/utils/get_durations_from_trained_model.py -i ljspeech.yaml -c matcha_ljspeech.ckpt
-```
-or simply:
-```bash
-matcha-tts-get-durations -i ljspeech.yaml -c matcha_ljspeech.ckpt
-```
----
-## Train using extracted alignments
-
-In the datasetconfig turn on load duration.
-Example: `ljspeech.yaml`
-```
-load_durations: True
-```
-or see an examples in configs/experiment/ljspeech_from_durations.yaml
-
-
 ## Citation information
 
 If you use our code or otherwise find this work useful, please cite our paper:

configs/data/ljspeech.yaml

@@ -1,7 +1,7 @@
 _target_: matcha.data.text_mel_datamodule.TextMelDataModule
 name: ljspeech
-train_filelist_path: data/LJSpeech-1.1/train.txt
+train_filelist_path: data/filelists/ljs_audio_text_train_filelist.txt
-valid_filelist_path: data/LJSpeech-1.1/val.txt
+valid_filelist_path: data/filelists/ljs_audio_text_val_filelist.txt
 batch_size: 32
 num_workers: 20
 pin_memory: True
@@ -19,4 +19,3 @@ data_statistics:  # Computed for ljspeech dataset
   mel_mean: -5.536622
   mel_std: 2.116101
 seed: ${seed}
-load_durations: false

configs/experiment/ljspeech_from_durations.yaml

@@ -1,19 +0,0 @@
-# @package _global_
-
-# to execute this experiment run:
-# python train.py experiment=multispeaker
-
-defaults:
-  - override /data: ljspeech.yaml
-
-# all parameters below will be merged with parameters from default configurations set above
-# this allows you to overwrite only specified parameters
-
-tags: ["ljspeech"]
-
-run_name: ljspeech
-
-
-data:
-  load_durations: True
-  batch_size: 64

configs/model/matcha.yaml

@@ -13,4 +13,3 @@ n_feats: 80
 data_statistics: ${data.data_statistics}
 out_size: null # Must be divisible by 4
 prior_loss: true
-use_precomputed_durations: ${data.load_durations}

matcha/VERSION

@@ -1 +1 @@
-0.0.6.0
+0.0.5.1

matcha/cli.py

@@ -48,7 +48,7 @@ def plot_spectrogram_to_numpy(spectrogram, filename):
 def process_text(i: int, text: str, device: torch.device):
     print(f"[{i}] - Input text: {text}")
     x = torch.tensor(
-        intersperse(text_to_sequence(text, ["english_cleaners2"])[0], 0),
+        intersperse(text_to_sequence(text, ["english_cleaners2"]), 0),
         dtype=torch.long,
         device=device,
     )[None]
@@ -326,13 +326,12 @@ def batched_synthesis(args, device, model, vocoder, denoiser, texts, spk):
     for i, batch in enumerate(dataloader):
         i = i + 1
         start_t = dt.datetime.now()
-        b = batch["x"].shape[0]
         output = model.synthesise(
             batch["x"].to(device),
             batch["x_lengths"].to(device),
             n_timesteps=args.steps,
             temperature=args.temperature,
-            spks=spk.expand(b) if spk is not None else spk,
+            spks=spk,
             length_scale=args.speaking_rate,
         )
 

matcha/data/text_mel_datamodule.py

@@ -1,8 +1,6 @@
 import random
-from pathlib import Path
 from typing import Any, Dict, Optional
 
-import numpy as np
 import torch
 import torchaudio as ta
 from lightning import LightningDataModule
@@ -41,7 +39,6 @@ class TextMelDataModule(LightningDataModule):
         f_max,
         data_statistics,
         seed,
-        load_durations,
     ):
         super().__init__()
 
@@ -71,7 +68,6 @@ class TextMelDataModule(LightningDataModule):
             self.hparams.f_max,
             self.hparams.data_statistics,
             self.hparams.seed,
-            self.hparams.load_durations,
         )
         self.validset = TextMelDataset(  # pylint: disable=attribute-defined-outside-init
             self.hparams.valid_filelist_path,
@@ -87,7 +83,6 @@ class TextMelDataModule(LightningDataModule):
             self.hparams.f_max,
             self.hparams.data_statistics,
             self.hparams.seed,
-            self.hparams.load_durations,
         )
 
     def train_dataloader(self):
@@ -114,7 +109,7 @@ class TextMelDataModule(LightningDataModule):
         """Clean up after fit or test."""
         pass  # pylint: disable=unnecessary-pass
 
-    def state_dict(self):
+    def state_dict(self):  # pylint: disable=no-self-use
         """Extra things to save to checkpoint."""
         return {}
 
@@ -139,7 +134,6 @@ class TextMelDataset(torch.utils.data.Dataset):
         f_max=8000,
         data_parameters=None,
         seed=None,
-        load_durations=False,
     ):
         self.filepaths_and_text = parse_filelist(filelist_path)
         self.n_spks = n_spks
@@ -152,8 +146,6 @@ class TextMelDataset(torch.utils.data.Dataset):
         self.win_length = win_length
         self.f_min = f_min
         self.f_max = f_max
-        self.load_durations = load_durations
-
         if data_parameters is not None:
             self.data_parameters = data_parameters
         else:
@@ -172,29 +164,10 @@ class TextMelDataset(torch.utils.data.Dataset):
             filepath, text = filepath_and_text[0], filepath_and_text[1]
             spk = None
 
-        text, cleaned_text = self.get_text(text, add_blank=self.add_blank)
+        text = self.get_text(text, add_blank=self.add_blank)
         mel = self.get_mel(filepath)
 
-        durations = self.get_durations(filepath, text) if self.load_durations else None
+        return {"x": text, "y": mel, "spk": spk}
-
-        return {"x": text, "y": mel, "spk": spk, "filepath": filepath, "x_text": cleaned_text, "durations": durations}
-
-    def get_durations(self, filepath, text):
-        filepath = Path(filepath)
-        data_dir, name = filepath.parent.parent, filepath.stem
-
-        try:
-            dur_loc = data_dir / "durations" / f"{name}.npy"
-            durs = torch.from_numpy(np.load(dur_loc).astype(int))
-
-        except FileNotFoundError as e:
-            raise FileNotFoundError(
-                f"Tried loading the durations but durations didn't exist at {dur_loc}, make sure you've generate the durations first using: python matcha/utils/get_durations_from_trained_model.py \n"
-            ) from e
-
-        assert len(durs) == len(text), f"Length of durations {len(durs)} and text {len(text)} do not match"
-
-        return durs
 
     def get_mel(self, filepath):
         audio, sr = ta.load(filepath)
@@ -214,11 +187,11 @@ class TextMelDataset(torch.utils.data.Dataset):
         return mel
 
     def get_text(self, text, add_blank=True):
-        text_norm, cleaned_text = text_to_sequence(text, self.cleaners)
+        text_norm = text_to_sequence(text, self.cleaners)
         if self.add_blank:
             text_norm = intersperse(text_norm, 0)
         text_norm = torch.IntTensor(text_norm)
-        return text_norm, cleaned_text
+        return text_norm
 
     def __getitem__(self, index):
         datapoint = self.get_datapoint(self.filepaths_and_text[index])
@@ -241,11 +214,8 @@ class TextMelBatchCollate:
 
         y = torch.zeros((B, n_feats, y_max_length), dtype=torch.float32)
         x = torch.zeros((B, x_max_length), dtype=torch.long)
-        durations = torch.zeros((B, x_max_length), dtype=torch.long)
-
         y_lengths, x_lengths = [], []
         spks = []
-        filepaths, x_texts = [], []
         for i, item in enumerate(batch):
             y_, x_ = item["y"], item["x"]
             y_lengths.append(y_.shape[-1])
@@ -253,22 +223,9 @@ class TextMelBatchCollate:
             y[i, :, : y_.shape[-1]] = y_
             x[i, : x_.shape[-1]] = x_
             spks.append(item["spk"])
-            filepaths.append(item["filepath"])
-            x_texts.append(item["x_text"])
-            if item["durations"] is not None:
-                durations[i, : item["durations"].shape[-1]] = item["durations"]
 
         y_lengths = torch.tensor(y_lengths, dtype=torch.long)
         x_lengths = torch.tensor(x_lengths, dtype=torch.long)
         spks = torch.tensor(spks, dtype=torch.long) if self.n_spks > 1 else None
 
-        return {
+        return {"x": x, "x_lengths": x_lengths, "y": y, "y_lengths": y_lengths, "spks": spks}
-            "x": x,
-            "x_lengths": x_lengths,
-            "y": y,
-            "y_lengths": y_lengths,
-            "spks": spks,
-            "filepaths": filepaths,
-            "x_texts": x_texts,
-            "durations": durations if not torch.eq(durations, 0).all() else None,
-        }

matcha/models/baselightningmodule.py

@@ -58,14 +58,13 @@ class BaseLightningClass(LightningModule, ABC):
         y, y_lengths = batch["y"], batch["y_lengths"]
         spks = batch["spks"]
 
-        dur_loss, prior_loss, diff_loss, *_ = self(
+        dur_loss, prior_loss, diff_loss = self(
             x=x,
             x_lengths=x_lengths,
             y=y,
             y_lengths=y_lengths,
             spks=spks,
             out_size=self.out_size,
-            durations=batch["durations"],
         )
         return {
             "dur_loss": dur_loss,

matcha/models/matcha_tts.py

@@ -35,7 +35,6 @@ class MatchaTTS(BaseLightningClass):  # 🍵
         optimizer=None,
         scheduler=None,
         prior_loss=True,
-        use_precomputed_durations=False,
     ):
         super().__init__()
 
@@ -47,7 +46,6 @@ class MatchaTTS(BaseLightningClass):  # 🍵
         self.n_feats = n_feats
         self.out_size = out_size
         self.prior_loss = prior_loss
-        self.use_precomputed_durations = use_precomputed_durations
 
         if n_spks > 1:
             self.spk_emb = torch.nn.Embedding(n_spks, spk_emb_dim)
@@ -149,7 +147,7 @@ class MatchaTTS(BaseLightningClass):  # 🍵
             "rtf": rtf,
         }
 
-    def forward(self, x, x_lengths, y, y_lengths, spks=None, out_size=None, cond=None, durations=None):
+    def forward(self, x, x_lengths, y, y_lengths, spks=None, out_size=None, cond=None):
         """
         Computes 3 losses:
             1. duration loss: loss between predicted token durations and those extracted by Monotinic Alignment Search (MAS).
@@ -181,20 +179,17 @@ class MatchaTTS(BaseLightningClass):  # 🍵
         y_mask = sequence_mask(y_lengths, y_max_length).unsqueeze(1).to(x_mask)
         attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2)
 
-        if self.use_precomputed_durations:
+        # Use MAS to find most likely alignment `attn` between text and mel-spectrogram
-            attn = generate_path(durations.squeeze(1), attn_mask.squeeze(1))
+        with torch.no_grad():
-        else:
+            const = -0.5 * math.log(2 * math.pi) * self.n_feats
-            # Use MAS to find most likely alignment `attn` between text and mel-spectrogram
+            factor = -0.5 * torch.ones(mu_x.shape, dtype=mu_x.dtype, device=mu_x.device)
-            with torch.no_grad():
+            y_square = torch.matmul(factor.transpose(1, 2), y**2)
-                const = -0.5 * math.log(2 * math.pi) * self.n_feats
+            y_mu_double = torch.matmul(2.0 * (factor * mu_x).transpose(1, 2), y)
-                factor = -0.5 * torch.ones(mu_x.shape, dtype=mu_x.dtype, device=mu_x.device)
+            mu_square = torch.sum(factor * (mu_x**2), 1).unsqueeze(-1)
-                y_square = torch.matmul(factor.transpose(1, 2), y**2)
+            log_prior = y_square - y_mu_double + mu_square + const
-                y_mu_double = torch.matmul(2.0 * (factor * mu_x).transpose(1, 2), y)
-                mu_square = torch.sum(factor * (mu_x**2), 1).unsqueeze(-1)
-                log_prior = y_square - y_mu_double + mu_square + const
 
-                attn = monotonic_align.maximum_path(log_prior, attn_mask.squeeze(1))
+            attn = monotonic_align.maximum_path(log_prior, attn_mask.squeeze(1))
-                attn = attn.detach()  # b, t_text, T_mel
+            attn = attn.detach()
 
         # Compute loss between predicted log-scaled durations and those obtained from MAS
         # refered to as prior loss in the paper
@@ -241,4 +236,4 @@ class MatchaTTS(BaseLightningClass):  # 🍵
         else:
             prior_loss = 0
 
-        return dur_loss, prior_loss, diff_loss, attn
+        return dur_loss, prior_loss, diff_loss

matcha/text/__init__.py

@@ -21,7 +21,7 @@ def text_to_sequence(text, cleaner_names):
     for symbol in clean_text:
         symbol_id = _symbol_to_id[symbol]
         sequence += [symbol_id]
-    return sequence, clean_text
+    return sequence
 
 
 def cleaned_text_to_sequence(cleaned_text):

matcha/text/cleaners.py

@@ -15,6 +15,7 @@ import logging
 import re
 
 import phonemizer
+import piper_phonemize
 from unidecode import unidecode
 
 # To avoid excessive logging we set the log level of the phonemizer package to Critical
@@ -105,17 +106,11 @@ def english_cleaners2(text):
     return phonemes
 
 
-# I am removing this due to incompatibility with several version of python
+def english_cleaners_piper(text):
-# However, if you want to use it, you can uncomment it
+    """Pipeline for English text, including abbreviation expansion. + punctuation + stress"""
-# and install piper-phonemize with the following command:
+    text = convert_to_ascii(text)
-# pip install piper-phonemize
+    text = lowercase(text)
-
+    text = expand_abbreviations(text)
-# import piper_phonemize
+    phonemes = "".join(piper_phonemize.phonemize_espeak(text=text, voice="en-US")[0])
-# def english_cleaners_piper(text):
+    phonemes = collapse_whitespace(phonemes)
-#     """Pipeline for English text, including abbreviation expansion. + punctuation + stress"""
+    return phonemes
-#     text = convert_to_ascii(text)
-#     text = lowercase(text)
-#     text = expand_abbreviations(text)
-#     phonemes = "".join(piper_phonemize.phonemize_espeak(text=text, voice="en-US")[0])
-#     phonemes = collapse_whitespace(phonemes)
-#     return phonemes

matcha/utils/generate_data_statistics.py

@@ -94,7 +94,6 @@ def main():
         cfg["batch_size"] = args.batch_size
         cfg["train_filelist_path"] = str(os.path.join(root_path, cfg["train_filelist_path"]))
         cfg["valid_filelist_path"] = str(os.path.join(root_path, cfg["valid_filelist_path"]))
-        cfg["load_durations"] = False
 
     text_mel_datamodule = TextMelDataModule(**cfg)
     text_mel_datamodule.setup()

matcha/utils/get_durations_from_trained_model.py

@@ -1,195 +0,0 @@
-r"""
-The file creates a pickle file where the values needed for loading of dataset is stored and the model can load it
-when needed.
-
-Parameters from hparam.py will be used
-"""
-import argparse
-import json
-import os
-import sys
-from pathlib import Path
-
-import lightning
-import numpy as np
-import rootutils
-import torch
-from hydra import compose, initialize
-from omegaconf import open_dict
-from torch import nn
-from tqdm.auto import tqdm
-
-from matcha.cli import get_device
-from matcha.data.text_mel_datamodule import TextMelDataModule
-from matcha.models.matcha_tts import MatchaTTS
-from matcha.utils.logging_utils import pylogger
-from matcha.utils.utils import get_phoneme_durations
-
-log = pylogger.get_pylogger(__name__)
-
-
-def save_durations_to_folder(
-    attn: torch.Tensor, x_length: int, y_length: int, filepath: str, output_folder: Path, text: str
-):
-    durations = attn.squeeze().sum(1)[:x_length].numpy()
-    durations_json = get_phoneme_durations(durations, text)
-    output = output_folder / Path(filepath).name.replace(".wav", ".npy")
-    with open(output.with_suffix(".json"), "w", encoding="utf-8") as f:
-        json.dump(durations_json, f, indent=4, ensure_ascii=False)
-
-    np.save(output, durations)
-
-
-@torch.inference_mode()
-def compute_durations(data_loader: torch.utils.data.DataLoader, model: nn.Module, device: torch.device, output_folder):
-    """Generate durations from the model for each datapoint and save it in a folder
-
-    Args:
-        data_loader (torch.utils.data.DataLoader): Dataloader
-        model (nn.Module): MatchaTTS model
-        device (torch.device): GPU or CPU
-    """
-
-    for batch in tqdm(data_loader, desc="🍵 Computing durations 🍵:"):
-        x, x_lengths = batch["x"], batch["x_lengths"]
-        y, y_lengths = batch["y"], batch["y_lengths"]
-        spks = batch["spks"]
-        x = x.to(device)
-        y = y.to(device)
-        x_lengths = x_lengths.to(device)
-        y_lengths = y_lengths.to(device)
-        spks = spks.to(device) if spks is not None else None
-
-        _, _, _, attn = model(
-            x=x,
-            x_lengths=x_lengths,
-            y=y,
-            y_lengths=y_lengths,
-            spks=spks,
-        )
-        attn = attn.cpu()
-        for i in range(attn.shape[0]):
-            save_durations_to_folder(
-                attn[i],
-                x_lengths[i].item(),
-                y_lengths[i].item(),
-                batch["filepaths"][i],
-                output_folder,
-                batch["x_texts"][i],
-            )
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "-i",
-        "--input-config",
-        type=str,
-        default="ljspeech.yaml",
-        help="The name of the yaml config file under configs/data",
-    )
-
-    parser.add_argument(
-        "-b",
-        "--batch-size",
-        type=int,
-        default="32",
-        help="Can have increased batch size for faster computation",
-    )
-
-    parser.add_argument(
-        "-f",
-        "--force",
-        action="store_true",
-        default=False,
-        required=False,
-        help="force overwrite the file",
-    )
-    parser.add_argument(
-        "-c",
-        "--checkpoint_path",
-        type=str,
-        required=True,
-        help="Path to the checkpoint file to load the model from",
-    )
-
-    parser.add_argument(
-        "-o",
-        "--output-folder",
-        type=str,
-        default=None,
-        help="Output folder to save the data statistics",
-    )
-
-    parser.add_argument(
-        "--cpu", action="store_true", help="Use CPU for inference, not recommended (default: use GPU if available)"
-    )
-
-    args = parser.parse_args()
-
-    with initialize(version_base="1.3", config_path="../../configs/data"):
-        cfg = compose(config_name=args.input_config, return_hydra_config=True, overrides=[])
-
-    root_path = rootutils.find_root(search_from=__file__, indicator=".project-root")
-
-    with open_dict(cfg):
-        del cfg["hydra"]
-        del cfg["_target_"]
-        cfg["seed"] = 1234
-        cfg["batch_size"] = args.batch_size
-        cfg["train_filelist_path"] = str(os.path.join(root_path, cfg["train_filelist_path"]))
-        cfg["valid_filelist_path"] = str(os.path.join(root_path, cfg["valid_filelist_path"]))
-        cfg["load_durations"] = False
-
-    if args.output_folder is not None:
-        output_folder = Path(args.output_folder)
-    else:
-        output_folder = Path(cfg["train_filelist_path"]).parent / "durations"
-
-    print(f"Output folder set to: {output_folder}")
-
-    if os.path.exists(output_folder) and not args.force:
-        print("Folder already exists. Use -f to force overwrite")
-        sys.exit(1)
-
-    output_folder.mkdir(parents=True, exist_ok=True)
-
-    print(f"Preprocessing: {cfg['name']} from training filelist: {cfg['train_filelist_path']}")
-    print("Loading model...")
-    device = get_device(args)
-    model = MatchaTTS.load_from_checkpoint(args.checkpoint_path, map_location=device)
-
-    text_mel_datamodule = TextMelDataModule(**cfg)
-    text_mel_datamodule.setup()
-    try:
-        print("Computing stats for training set if exists...")
-        train_dataloader = text_mel_datamodule.train_dataloader()
-        compute_durations(train_dataloader, model, device, output_folder)
-    except lightning.fabric.utilities.exceptions.MisconfigurationException:
-        print("No training set found")
-
-    try:
-        print("Computing stats for validation set if exists...")
-        val_dataloader = text_mel_datamodule.val_dataloader()
-        compute_durations(val_dataloader, model, device, output_folder)
-    except lightning.fabric.utilities.exceptions.MisconfigurationException:
-        print("No validation set found")
-
-    try:
-        print("Computing stats for test set if exists...")
-        test_dataloader = text_mel_datamodule.test_dataloader()
-        compute_durations(test_dataloader, model, device, output_folder)
-    except lightning.fabric.utilities.exceptions.MisconfigurationException:
-        print("No test set found")
-
-    print(f"[+] Done! Data statistics saved to: {output_folder}")
-
-
-if __name__ == "__main__":
-    # Helps with generating durations for the dataset to train other architectures
-    # that cannot learn to align due to limited size of dataset
-    # Example usage:
-    # python python matcha/utils/get_durations_from_trained_model.py -i ljspeech.yaml -c pretrained_model
-    # This will create a folder in data/processed_data/durations/ljspeech with the durations
-    main()

matcha/utils/utils.py

@@ -2,7 +2,6 @@ import os
 import sys
 import warnings
 from importlib.util import find_spec
-from math import ceil
 from pathlib import Path
 from typing import Any, Callable, Dict, Tuple
 
@@ -218,42 +217,3 @@ def assert_model_downloaded(checkpoint_path, url, use_wget=True):
         gdown.download(url=url, output=checkpoint_path, quiet=False, fuzzy=True)
     else:
         wget.download(url=url, out=checkpoint_path)
-
-
-def get_phoneme_durations(durations, phones):
-    prev = durations[0]
-    merged_durations = []
-    # Convolve with stride 2
-    for i in range(1, len(durations), 2):
-        if i == len(durations) - 2:
-            # if it is last take full value
-            next_half = durations[i + 1]
-        else:
-            next_half = ceil(durations[i + 1] / 2)
-
-        curr = prev + durations[i] + next_half
-        prev = durations[i + 1] - next_half
-        merged_durations.append(curr)
-
-    assert len(phones) == len(merged_durations)
-    assert len(merged_durations) == (len(durations) - 1) // 2
-
-    merged_durations = torch.cumsum(torch.tensor(merged_durations), 0, dtype=torch.long)
-    start = torch.tensor(0)
-    duration_json = []
-    for i, duration in enumerate(merged_durations):
-        duration_json.append(
-            {
-                phones[i]: {
-                    "starttime": start.item(),
-                    "endtime": duration.item(),
-                    "duration": duration.item() - start.item(),
-                }
-            }
-        )
-        start = duration
-
-    assert list(duration_json[-1].values())[0]["endtime"] == sum(
-        durations
-    ), f"{list(duration_json[-1].values())[0]['endtime'],  sum(durations)}"
-    return duration_json

requirements.txt

@@ -35,10 +35,11 @@ torchaudio
 matplotlib
 pandas
 conformer==0.3.2
-diffusers==0.25.0
+diffusers==0.27.2
 notebook
 ipywidgets
-gradio==3.43.2
+gradio
 gdown
 wget
 seaborn
+piper_phonemize

setup.py

@@ -38,7 +38,6 @@ setup(
             "matcha-data-stats=matcha.utils.generate_data_statistics:main",
             "matcha-tts=matcha.cli:cli",
             "matcha-tts-app=matcha.app:main",
-            "matcha-tts-get-durations=matcha.utils.get_durations_from_trained_model:main",
         ]
     },
     ext_modules=cythonize(exts, language_level=3),

synthesis.ipynb

@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "148f4bc0-c28e-4670-9a5e-4c7928ab8992",
    "metadata": {},
    "outputs": [
@@ -192,7 +192,7 @@
    "source": [
     "@torch.inference_mode()\n",
     "def process_text(text: str):\n",
-    "    x = torch.tensor(intersperse(text_to_sequence(text, ['english_cleaners2'])[0], 0),dtype=torch.long, device=device)[None]\n",
+    "    x = torch.tensor(intersperse(text_to_sequence(text, ['english_cleaners2']), 0),dtype=torch.long, device=device)[None]\n",
     "    x_lengths = torch.tensor([x.shape[-1]],dtype=torch.long, device=device)\n",
     "    x_phones = sequence_to_text(x.squeeze(0).tolist())\n",
     "    return {\n",