minor fix

.gitignore

@@ -161,4 +161,3 @@ generator_v1
 g_02500000
 gradio_cached_examples/
 synth_output/
-/data

.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: v6.0.0
+    rev: v4.5.0
     hooks:
       # list of supported hooks: https://pre-commit.com/hooks.html
       - id: trailing-whitespace
@@ -17,29 +17,29 @@ repos:
       - id: check-added-large-files
 
   # python code formatting
-  - repo: https://github.com/psf/black-pre-commit-mirror
+  - repo: https://github.com/psf/black
-    rev: 26.1.0
+    rev: 23.12.1
     hooks:
       - id: black
         args: [--line-length, "120"]
 
   # python import sorting
   - repo: https://github.com/PyCQA/isort
-    rev: 7.0.0
+    rev: 5.13.2
     hooks:
       - id: isort
         args: ["--profile", "black", "--filter-files"]
 
   # python upgrading syntax to newer version
   - repo: https://github.com/asottile/pyupgrade
-    rev: v3.21.2
+    rev: v3.15.0
     hooks:
       - id: pyupgrade
         args: [--py38-plus]
 
   # python check (PEP8), programming errors and code complexity
   - repo: https://github.com/PyCQA/flake8
-    rev: 7.3.0
+    rev: 7.0.0
     hooks:
       - id: flake8
         args:
@@ -54,6 +54,6 @@ repos:
 
   # pylint
   - repo: https://github.com/pycqa/pylint
-    rev: v4.0.4
+    rev: v3.0.3
     hooks:
     -   id: pylint

README.md

@@ -10,7 +10,7 @@
 [![hydra](https://img.shields.io/badge/Config-Hydra_1.3-89b8cd)](https://hydra.cc/)
 [![black](https://img.shields.io/badge/Code%20Style-Black-black.svg?labelColor=gray)](https://black.readthedocs.io/en/stable/)
 [![isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)
-[![PyPI Downloads](https://static.pepy.tech/personalized-badge/matcha-tts?period=total&units=INTERNATIONAL_SYSTEM&left_color=BLACK&right_color=GREEN&left_text=downloads)](https://pepy.tech/projects/matcha-tts)
+
 <p style="text-align: center;">
   <img src="https://shivammehta25.github.io/Matcha-TTS/images/logo.png" height="128"/>
 </p>
@@ -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/hi-fi_en-US_female.yaml

@@ -5,8 +5,8 @@ defaults:
 # Dataset URL: https://ast-astrec.nict.go.jp/en/release/hi-fi-captain/
 _target_: matcha.data.text_mel_datamodule.TextMelDataModule
 name: hi-fi_en-US_female
-train_filelist_path: data/hi-fi_en-US_female/train.txt
+train_filelist_path: data/filelists/hi-fi-captain-en-us-female_train.txt
-valid_filelist_path: data/hi-fi_en-US_female/val.txt
+valid_filelist_path: data/filelists/hi-fi-captain-en-us-female_val.txt
 batch_size: 32
 cleaners: [english_cleaners_piper]
 data_statistics:  # Computed for this dataset

configs/data/joe_spont_only.yaml

@@ -0,0 +1,10 @@
+defaults:
+  - ljspeech
+  - _self_
+
+name: joe_spont_only
+train_filelist_path: data/filelists/joe_spontonly_train.txt
+valid_filelist_path: data/filelists/joe_spontonly_val.txt
+data_statistics:
+  mel_mean: -5.882903
+  mel_std: 2.458284

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/data/ryan.yaml

@@ -0,0 +1,10 @@
+defaults:
+  - ljspeech
+  - _self_
+
+name: ryan
+train_filelist_path: data/filelists/ryan_train.csv
+valid_filelist_path: data/filelists/ryan_val.csv
+data_statistics:
+  mel_mean: -4.715779
+  mel_std: 2.124502

configs/data/tsg2.yaml

@@ -0,0 +1,10 @@
+defaults:
+  - ljspeech
+  - _self_
+
+name: tsg2
+train_filelist_path: data/filelists/cormac_train.txt
+valid_filelist_path: data/filelists/cormac_val.txt
+data_statistics:
+  mel_mean: -5.536622
+  mel_std: 2.116101

configs/experiment/joe_det_dur.yaml

@@ -0,0 +1,14 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=multispeaker
+
+defaults:
+  - override /data: joe_spont_only.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["joe"]
+
+run_name: joe_det_dur

configs/experiment/joe_stoc_dur.yaml

@@ -0,0 +1,20 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=multispeaker
+
+defaults:
+  - override /data: joe_spont_only.yaml
+  - override /model/duration_predictor: flow_matching.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["joe"]
+
+
+run_name: joe_stoc_dur
+
+model:
+  duration_predictor:
+    p_dropout: 0.2

configs/experiment/ljspeech_stoc_dur.yaml

@@ -5,15 +5,12 @@
 
 defaults:
   - override /data: ljspeech.yaml
+  - override /model/duration_predictor: flow_matching.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/experiment/ryan_det_dur.yaml

@@ -0,0 +1,18 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=multispeaker
+
+defaults:
+  - override /data: ryan.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["ryan"]
+
+run_name: ryan_det_dur
+
+
+trainer:
+  max_epochs: 3000

configs/experiment/ryan_stoc_dur.yaml

@@ -0,0 +1,24 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=multispeaker
+
+defaults:
+  - override /data: ryan.yaml
+  - override /model/duration_predictor: flow_matching.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["ryan"]
+
+
+run_name: ryan_stoc_dur
+
+model:
+  duration_predictor:
+    p_dropout: 0.2
+
+
+trainer:
+  max_epochs: 3000

configs/experiment/tsg2_det_dur.yaml

@@ -0,0 +1,14 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=multispeaker
+
+defaults:
+  - override /data: tsg2.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["tsg2"]
+
+run_name: tsg2_det_dur

configs/experiment/tsg2_stoc_dur.yaml

@@ -0,0 +1,20 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=multispeaker
+
+defaults:
+  - override /data: tsg2.yaml
+  - override /model/duration_predictor: flow_matching.yaml
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+tags: ["tsg2"]
+
+
+run_name: tsg2_stoc_dur
+
+model:
+  duration_predictor:
+    p_dropout: 0.5

configs/model/duration_predictor/deterministic.yaml

@@ -0,0 +1,7 @@
+name: deterministic
+n_spks: ${model.n_spks}
+spk_emb_dim: ${model.spk_emb_dim}
+filter_channels: 256
+kernel_size: 3
+n_channels: ${model.encoder.encoder_params.n_channels}
+p_dropout: ${model.encoder.encoder_params.p_dropout}

configs/model/duration_predictor/flow_matching.yaml

@@ -0,0 +1,7 @@
+defaults:
+  - deterministic.yaml
+  - _self_
+
+sigma_min: 1e-4
+n_steps: 10
+name: flow_matching

configs/model/encoder/default.yaml

@@ -3,16 +3,8 @@ encoder_params:
   n_feats: ${model.n_feats}
   n_channels: 192
   filter_channels: 768
-  filter_channels_dp: 256
   n_heads: 2
   n_layers: 6
   kernel_size: 3
   p_dropout: 0.1
-  spk_emb_dim: 64
-  n_spks: 1
   prenet: true
-
-duration_predictor_params:
-  filter_channels_dp: ${model.encoder.encoder_params.filter_channels_dp}
-  kernel_size: 3
-  p_dropout: ${model.encoder.encoder_params.p_dropout}

configs/model/matcha.yaml

@@ -1,6 +1,7 @@
 defaults:
   - _self_
   - encoder: default.yaml
+  - duration_predictor: deterministic.yaml
   - decoder: default.yaml
   - cfm: default.yaml
   - optimizer: adam.yaml
@@ -13,4 +14,3 @@ 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}

data

@@ -0,0 +1 @@
+/home/smehta/Projects/Speech-Backbones/Grad-TTS/data

matcha/VERSION

@@ -1 +1 @@
-0.0.7.2
+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]
@@ -114,10 +114,10 @@ def load_matcha(model_name, checkpoint_path, device):
     return model
 
 
-def to_waveform(mel, vocoder, denoiser=None, denoiser_strength=0.00025):
+def to_waveform(mel, vocoder, denoiser=None):
     audio = vocoder(mel).clamp(-1, 1)
     if denoiser is not None:
-        audio = denoiser(audio.squeeze(), strength=denoiser_strength).cpu().squeeze()
+        audio = denoiser(audio.squeeze(), strength=0.00025).cpu().squeeze()
 
     return audio.cpu().squeeze()
 
@@ -227,7 +227,7 @@ def cli():
     parser.add_argument(
         "--vocoder",
         type=str,
-        default=None,
+        default="hifigan_univ_v1",
         help="Vocoder to use (default: will use the one suggested with the pretrained model))",
         choices=VOCODER_URLS.keys(),
     )
@@ -326,17 +326,16 @@ 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,
         )
 
-        output["waveform"] = to_waveform(output["mel"], vocoder, denoiser, args.denoiser_strength)
+        output["waveform"] = to_waveform(output["mel"], vocoder, denoiser)
         t = (dt.datetime.now() - start_t).total_seconds()
         rtf_w = t * 22050 / (output["waveform"].shape[-1])
         print(f"[🍵-Batch: {i}] Matcha-TTS RTF: {output['rtf']:.4f}")
@@ -377,7 +376,7 @@ def unbatched_synthesis(args, device, model, vocoder, denoiser, texts, spk):
             spks=spk,
             length_scale=args.speaking_rate,
         )
-        output["waveform"] = to_waveform(output["mel"], vocoder, denoiser, args.denoiser_strength)
+        output["waveform"] = to_waveform(output["mel"], vocoder, denoiser)
         # RTF with HiFiGAN
         t = (dt.datetime.now() - start_t).total_seconds()
         rtf_w = t * 22050 / (output["waveform"].shape[-1])

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):
@@ -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:
@@ -175,26 +167,7 @@ class TextMelDataset(torch.utils.data.Dataset):
         text, cleaned_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, "filepath": filepath, "x_text": cleaned_text}
-
-        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)
@@ -241,8 +214,6 @@ 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 = [], []
@@ -255,8 +226,6 @@ class TextMelBatchCollate:
             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)
@@ -270,5 +239,4 @@ class TextMelBatchCollate:
             "spks": spks,
             "filepaths": filepaths,
             "x_texts": x_texts,
-            "durations": durations if not torch.eq(durations, 0).all() else None,
         }

matcha/hifigan/denoiser.py

@@ -1,14 +1,9 @@
 # Code modified from Rafael Valle's implementation https://github.com/NVIDIA/waveglow/blob/5bc2a53e20b3b533362f974cfa1ea0267ae1c2b1/denoiser.py
 
 """Waveglow style denoiser can be used to remove the artifacts from the HiFiGAN generated audio."""
-
 import torch
 
 
-class ModeException(Exception):
-    pass
-
-
 class Denoiser(torch.nn.Module):
     """Removes model bias from audio produced with waveglow"""
 
@@ -25,7 +20,7 @@ class Denoiser(torch.nn.Module):
         elif mode == "normal":
             mel_input = torch.randn((1, 80, 88), dtype=dtype, device=device)
         else:
-            raise ModeException(f"Mode {mode} if not supported")
+            raise Exception(f"Mode {mode} if not supported")
 
         def stft_fn(audio, n_fft, hop_length, win_length, window):
             spec = torch.stft(

matcha/hifigan/meldataset.py

@@ -55,7 +55,7 @@ def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin,
     if torch.max(y) > 1.0:
         print("max value is ", torch.max(y))
 
-    global mel_basis, hann_window  # pylint: disable=global-statement,global-variable-not-assigned
+    global mel_basis, hann_window  # pylint: disable=global-statement
     if fmax not in mel_basis:
         mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
         mel_basis[str(fmax) + "_" + str(y.device)] = torch.from_numpy(mel).float().to(y.device)

matcha/hifigan/models.py

@@ -1,7 +1,7 @@
 """ from https://github.com/jik876/hifi-gan """
 
 import torch
-import torch.nn as nn  # pylint: disable=consider-using-from-import
+import torch.nn as nn
 import torch.nn.functional as F
 from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
 from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm

matcha/models/baselightningmodule.py

@@ -2,7 +2,6 @@
 This is a base lightning module that can be used to train a model.
 The benefit of this abstraction is that all the logic outside of model definition can be reused for different models.
 """
-
 import inspect
 from abc import ABC
 from typing import Any, Dict
@@ -66,7 +65,6 @@ class BaseLightningClass(LightningModule, ABC):
             y_lengths=y_lengths,
             spks=spks,
             out_size=self.out_size,
-            durations=batch["durations"],
         )
         return {
             "dur_loss": dur_loss,

matcha/models/components/decoder.py

@@ -2,7 +2,7 @@ import math
 from typing import Optional
 
 import torch
-import torch.nn as nn  # pylint: disable=consider-using-from-import
+import torch.nn as nn
 import torch.nn.functional as F
 from conformer import ConformerBlock
 from diffusers.models.activations import get_activation

matcha/models/components/duration_predictors.py

@@ -0,0 +1,448 @@
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import pack
+
+from matcha.models.components.decoder import SinusoidalPosEmb, TimestepEmbedding
+from matcha.models.components.text_encoder import LayerNorm
+
+# Define available networks
+
+
+class DurationPredictorNetwork(nn.Module):
+    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.p_dropout = p_dropout
+
+        self.drop = torch.nn.Dropout(p_dropout)
+        self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_1 = LayerNorm(filter_channels)
+        self.conv_2 = torch.nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_2 = LayerNorm(filter_channels)
+        self.proj = torch.nn.Conv1d(filter_channels, 1, 1)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+class DurationPredictorNetworkWithTimeStep(nn.Module):
+    """Similar architecture but with a time embedding support"""
+
+    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout):
+        super().__init__()
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.p_dropout = p_dropout
+
+        self.time_embeddings = SinusoidalPosEmb(filter_channels)
+        self.time_mlp = TimestepEmbedding(
+            in_channels=filter_channels,
+            time_embed_dim=filter_channels,
+            act_fn="silu",
+        )
+
+        self.drop = torch.nn.Dropout(p_dropout)
+        self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_1 = LayerNorm(filter_channels)
+        self.conv_2 = torch.nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.norm_2 = LayerNorm(filter_channels)
+        self.proj = torch.nn.Conv1d(filter_channels, 1, 1)
+
+    def forward(self, x, x_mask, enc_outputs, t):
+        t = self.time_embeddings(t)
+        t = self.time_mlp(t).unsqueeze(-1)
+
+        x = pack([x, enc_outputs], "b * t")[0]
+
+        x = self.conv_1(x * x_mask)
+        x = torch.relu(x)
+        x = x + t
+        x = self.norm_1(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        x = torch.relu(x)
+        x = x + t
+        x = self.norm_2(x)
+        x = self.drop(x)
+        x = self.proj(x * x_mask)
+        return x * x_mask
+
+
+# Define available methods to compute loss
+
+# Simple MSE deterministic
+
+
+class DeterministicDurationPredictor(nn.Module):
+    def __init__(self, params):
+        super().__init__()
+        self.estimator = DurationPredictorNetwork(
+            params.n_channels + (params.spk_emb_dim if params.n_spks > 1 else 0),
+            params.filter_channels,
+            params.kernel_size,
+            params.p_dropout,
+        )
+
+    @torch.inference_mode()
+    def forward(self, x, x_mask):
+        return self.estimator(x, x_mask)
+
+    def compute_loss(self, durations, enc_outputs, x_mask):
+        return F.mse_loss(self.estimator(enc_outputs, x_mask), durations, reduction="sum") / torch.sum(x_mask)
+
+
+# Flow Matching duration predictor
+
+
+class FlowMatchingDurationPrediction(nn.Module):
+    def __init__(self, params) -> None:
+        super().__init__()
+
+        self.estimator = DurationPredictorNetworkWithTimeStep(
+            1
+            + params.n_channels
+            + (
+                params.spk_emb_dim if params.n_spks > 1 else 0
+            ),  # 1 for the durations and n_channels for encoder outputs
+            params.filter_channels,
+            params.kernel_size,
+            params.p_dropout,
+        )
+        self.sigma_min = params.sigma_min
+        self.n_steps = params.n_steps
+
+    @torch.inference_mode()
+    def forward(self, enc_outputs, mask, n_timesteps=500, temperature=1):
+        """Forward diffusion
+
+        Args:
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            n_timesteps (int): number of diffusion steps
+            temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+            cond: Not used but kept for future purposes
+
+        Returns:
+            sample: generated mel-spectrogram
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+        if n_timesteps is None:
+            n_timesteps = self.n_steps
+
+        b, _, t = enc_outputs.shape
+        z = torch.randn((b, 1, t), device=enc_outputs.device, dtype=enc_outputs.dtype) * temperature
+        t_span = torch.linspace(0, 1, n_timesteps + 1, device=enc_outputs.device)
+        return self.solve_euler(z, t_span=t_span, enc_outputs=enc_outputs, mask=mask)
+
+    def solve_euler(self, x, t_span, enc_outputs, mask):
+        """
+        Fixed euler solver for ODEs.
+        Args:
+            x (torch.Tensor): random noise
+            t_span (torch.Tensor): n_timesteps interpolated
+                shape: (n_timesteps + 1,)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): output_mask
+                shape: (batch_size, 1, mel_timesteps)
+            spks (torch.Tensor, optional): speaker ids. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+        """
+        t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
+
+        # I am storing this because I can later plot it by putting a debugger here and saving it to a file
+        # Or in future might add like a return_all_steps flag
+        sol = []
+
+        for step in range(1, len(t_span)):
+            dphi_dt = self.estimator(x, mask, enc_outputs, t)
+
+            x = x + dt * dphi_dt
+            t = t + dt
+            sol.append(x)
+            if step < len(t_span) - 1:
+                dt = t_span[step + 1] - t
+
+        return sol[-1]
+
+    def compute_loss(self, x1, enc_outputs, mask):
+        """Computes diffusion loss
+
+        Args:
+            x1 (torch.Tensor): Target
+                shape: (batch_size, n_feats, mel_timesteps)
+            mask (torch.Tensor): target mask
+                shape: (batch_size, 1, mel_timesteps)
+            mu (torch.Tensor): output of encoder
+                shape: (batch_size, n_feats, mel_timesteps)
+            spks (torch.Tensor, optional): speaker embedding. Defaults to None.
+                shape: (batch_size, spk_emb_dim)
+
+        Returns:
+            loss: conditional flow matching loss
+            y: conditional flow
+                shape: (batch_size, n_feats, mel_timesteps)
+        """
+        enc_outputs = enc_outputs.detach()  # don't update encoder from the duration predictor
+        b, _, t = enc_outputs.shape
+
+        # random timestep
+        t = torch.rand([b, 1, 1], device=enc_outputs.device, dtype=enc_outputs.dtype)
+        # sample noise p(x_0)
+        z = torch.randn_like(x1)
+
+        y = (1 - (1 - self.sigma_min) * t) * z + t * x1
+        u = x1 - (1 - self.sigma_min) * z
+
+        loss = F.mse_loss(self.estimator(y, mask, enc_outputs, t.squeeze()), u, reduction="sum") / (
+            torch.sum(mask) * u.shape[1]
+        )
+        return loss
+
+
+# VITS discrete normalising flow based duration predictor
+
+
+class Log(nn.Module):
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+            logdet = torch.sum(-y, [1, 2])
+            return y, logdet
+        else:
+            x = torch.exp(x) * x_mask
+            return x
+
+
+class ElementwiseAffine(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.channels = channels
+        self.m = nn.Parameter(torch.zeros(channels, 1))
+        self.logs = nn.Parameter(torch.zeros(channels, 1))
+
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = self.m + torch.exp(self.logs) * x
+            y = y * x_mask
+            logdet = torch.sum(self.logs * x_mask, [1, 2])
+            return y, logdet
+        else:
+            x = (x - self.m) * torch.exp(-self.logs) * x_mask
+            return x
+
+
+class DDSConv(nn.Module):
+    """
+    Dialted and Depth-Separable Convolution
+    """
+
+    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
+        super().__init__()
+        self.channels = channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+
+        self.drop = nn.Dropout(p_dropout)
+        self.convs_sep = nn.ModuleList()
+        self.convs_1x1 = nn.ModuleList()
+        self.norms_1 = nn.ModuleList()
+        self.norms_2 = nn.ModuleList()
+        for i in range(n_layers):
+            dilation = kernel_size**i
+            padding = (kernel_size * dilation - dilation) // 2
+            self.convs_sep.append(
+                nn.Conv1d(channels, channels, kernel_size, groups=channels, dilation=dilation, padding=padding)
+            )
+            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+            self.norms_1.append(LayerNorm(channels))
+            self.norms_2.append(LayerNorm(channels))
+
+    def forward(self, x, x_mask, g=None):
+        if g is not None:
+            x = x + g
+        for i in range(self.n_layers):
+            y = self.convs_sep[i](x * x_mask)
+            y = self.norms_1[i](y)
+            y = F.gelu(y)
+            y = self.convs_1x1[i](y)
+            y = self.norms_2[i](y)
+            y = F.gelu(y)
+            y = self.drop(y)
+            x = x + y
+        return x * x_mask
+
+
+class ConvFlow(nn.Module):
+    def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
+        super().__init__()
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.num_bins = num_bins
+        self.tail_bound = tail_bound
+        self.half_channels = in_channels // 2
+
+        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
+        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
+        self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0)
+        h = self.convs(h, x_mask, g=g)
+        h = self.proj(h) * x_mask
+
+        b, c, t = x0.shape
+        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
+
+        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
+        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(self.filter_channels)
+        unnormalized_derivatives = h[..., 2 * self.num_bins :]
+
+        x1, logabsdet = piecewise_rational_quadratic_transform(
+            x1,
+            unnormalized_widths,
+            unnormalized_heights,
+            unnormalized_derivatives,
+            inverse=reverse,
+            tails="linear",
+            tail_bound=self.tail_bound,
+        )
+
+        x = torch.cat([x0, x1], 1) * x_mask
+        logdet = torch.sum(logabsdet * x_mask, [1, 2])
+        if not reverse:
+            return x, logdet
+        else:
+            return x
+
+
+class StochasticDurationPredictor(nn.Module):
+    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
+        super().__init__()
+        filter_channels = in_channels  # it needs to be removed from future version.
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.log_flow = Log()
+        self.flows = nn.ModuleList()
+        self.flows.append(ElementwiseAffine(2))
+        for i in range(n_flows):
+            self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+            self.flows.append(modules.Flip())
+
+        self.post_pre = nn.Conv1d(1, filter_channels, 1)
+        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+        self.post_flows = nn.ModuleList()
+        self.post_flows.append(modules.ElementwiseAffine(2))
+        for i in range(4):
+            self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+            self.post_flows.append(modules.Flip())
+
+        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+        self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+    def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
+        x = torch.detach(x)
+        x = self.pre(x)
+        if g is not None:
+            g = torch.detach(g)
+            x = x + self.cond(g)
+        x = self.convs(x, x_mask)
+        x = self.proj(x) * x_mask
+
+        if not reverse:
+            flows = self.flows
+            assert w is not None
+
+            logdet_tot_q = 0
+            h_w = self.post_pre(w)
+            h_w = self.post_convs(h_w, x_mask)
+            h_w = self.post_proj(h_w) * x_mask
+            e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
+            z_q = e_q
+            for flow in self.post_flows:
+                z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
+                logdet_tot_q += logdet_q
+            z_u, z1 = torch.split(z_q, [1, 1], 1)
+            u = torch.sigmoid(z_u) * x_mask
+            z0 = (w - u) * x_mask
+            logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
+            logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q**2)) * x_mask, [1, 2]) - logdet_tot_q
+
+            logdet_tot = 0
+            z0, logdet = self.log_flow(z0, x_mask)
+            logdet_tot += logdet
+            z = torch.cat([z0, z1], 1)
+            for flow in flows:
+                z, logdet = flow(z, x_mask, g=x, reverse=reverse)
+                logdet_tot = logdet_tot + logdet
+            nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z**2)) * x_mask, [1, 2]) - logdet_tot
+            return nll + logq  # [b]
+        else:
+            flows = list(reversed(self.flows))
+            flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
+            z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
+            for flow in flows:
+                z = flow(z, x_mask, g=x, reverse=reverse)
+            z0, z1 = torch.split(z, [1, 1], 1)
+            logw = z0
+            return logw
+
+
+# Meta class to wrap all duration predictors
+
+
+class DP(nn.Module):
+    def __init__(self, params):
+        super().__init__()
+        self.name = params.name
+
+        if params.name == "deterministic":
+            self.dp = DeterministicDurationPredictor(
+                params,
+            )
+        elif params.name == "flow_matching":
+            self.dp = FlowMatchingDurationPrediction(
+                params,
+            )
+        else:
+            raise ValueError(f"Invalid duration predictor configuration: {params.name}")
+
+    @torch.inference_mode()
+    def forward(self, enc_outputs, mask):
+        return self.dp(enc_outputs, mask)
+
+    def compute_loss(self, durations, enc_outputs, mask):
+        return self.dp.compute_loss(durations, enc_outputs, mask)

matcha/models/components/text_encoder.py

@@ -3,10 +3,10 @@
 import math
 
 import torch
-import torch.nn as nn  # pylint: disable=consider-using-from-import
+import torch.nn as nn
 from einops import rearrange
 
-import matcha.utils as utils  # pylint: disable=consider-using-from-import
+import matcha.utils as utils
 from matcha.utils.model import sequence_mask
 
 log = utils.get_pylogger(__name__)
@@ -67,33 +67,6 @@ class ConvReluNorm(nn.Module):
         return x * x_mask
 
 
-class DurationPredictor(nn.Module):
-    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.p_dropout = p_dropout
-
-        self.drop = torch.nn.Dropout(p_dropout)
-        self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.norm_1 = LayerNorm(filter_channels)
-        self.conv_2 = torch.nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.norm_2 = LayerNorm(filter_channels)
-        self.proj = torch.nn.Conv1d(filter_channels, 1, 1)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_1(x)
-        x = self.drop(x)
-        x = self.conv_2(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_2(x)
-        x = self.drop(x)
-        x = self.proj(x * x_mask)
-        return x * x_mask
-
-
 class RotaryPositionalEmbeddings(nn.Module):
     """
     ## RoPE module
@@ -330,7 +303,6 @@ class TextEncoder(nn.Module):
         self,
         encoder_type,
         encoder_params,
-        duration_predictor_params,
         n_vocab,
         n_spks=1,
         spk_emb_dim=128,
@@ -368,12 +340,6 @@ class TextEncoder(nn.Module):
         )
 
         self.proj_m = torch.nn.Conv1d(self.n_channels + (spk_emb_dim if n_spks > 1 else 0), self.n_feats, 1)
-        self.proj_w = DurationPredictor(
-            self.n_channels + (spk_emb_dim if n_spks > 1 else 0),
-            duration_predictor_params.filter_channels_dp,
-            duration_predictor_params.kernel_size,
-            duration_predictor_params.p_dropout,
-        )
 
     def forward(self, x, x_lengths, spks=None):
         """Run forward pass to the transformer based encoder and duration predictor
@@ -404,7 +370,7 @@ class TextEncoder(nn.Module):
         x = self.encoder(x, x_mask)
         mu = self.proj_m(x) * x_mask
 
-        x_dp = torch.detach(x)
+        # x_dp = torch.detach(x)
-        logw = self.proj_w(x_dp, x_mask)
+        # logw = self.proj_w(x_dp, x_mask)
 
-        return mu, logw, x_mask
+        return mu, x, x_mask

matcha/models/components/transformer.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, Optional
 
 import torch
-import torch.nn as nn  # pylint: disable=consider-using-from-import
+import torch.nn as nn
 from diffusers.models.attention import (
     GEGLU,
     GELU,

matcha/models/matcha_tts.py

@@ -7,11 +7,11 @@ import torch
 import matcha.utils.monotonic_align as monotonic_align  # pylint: disable=consider-using-from-import
 from matcha import utils
 from matcha.models.baselightningmodule import BaseLightningClass
+from matcha.models.components.duration_predictors import DP
 from matcha.models.components.flow_matching import CFM
 from matcha.models.components.text_encoder import TextEncoder
 from matcha.utils.model import (
     denormalize,
-    duration_loss,
     fix_len_compatibility,
     generate_path,
     sequence_mask,
@@ -28,6 +28,7 @@ class MatchaTTS(BaseLightningClass):  # 🍵
         spk_emb_dim,
         n_feats,
         encoder,
+        duration_predictor,
         decoder,
         cfm,
         data_statistics,
@@ -35,7 +36,6 @@ class MatchaTTS(BaseLightningClass):  # 🍵
         optimizer=None,
         scheduler=None,
         prior_loss=True,
-        use_precomputed_durations=False,
     ):
         super().__init__()
 
@@ -47,7 +47,6 @@ 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)
@@ -55,12 +54,13 @@ class MatchaTTS(BaseLightningClass):  # 🍵
         self.encoder = TextEncoder(
             encoder.encoder_type,
             encoder.encoder_params,
-            encoder.duration_predictor_params,
             n_vocab,
             n_spks,
             spk_emb_dim,
         )
 
+        self.dp = DP(duration_predictor)
+
         self.decoder = CFM(
             in_channels=2 * encoder.encoder_params.n_feats,
             out_channel=encoder.encoder_params.n_feats,
@@ -106,7 +106,6 @@ class MatchaTTS(BaseLightningClass):  # 🍵
                 # Lengths of mel spectrograms
                 "rtf": float,
                 # Real-time factor
-            }
         """
         # For RTF computation
         t = dt.datetime.now()
@@ -115,11 +114,15 @@ class MatchaTTS(BaseLightningClass):  # 🍵
             # Get speaker embedding
             spks = self.spk_emb(spks.long())
 
-        # Get encoder_outputs `mu_x` and log-scaled token durations `logw`
+        # Get encoder_outputs `mu_x` and encoded text `enc_output`
-        mu_x, logw, x_mask = self.encoder(x, x_lengths, spks)
+        mu_x, enc_output, x_mask = self.encoder(x, x_lengths, spks)
+
+        # Get log-scaled token durations `logw`
+        logw = self.dp(enc_output, x_mask)
 
         w = torch.exp(logw) * x_mask
         w_ceil = torch.ceil(w) * length_scale
+        # print(w_ceil)
         y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
         y_max_length = y_lengths.max()
         y_max_length_ = fix_len_compatibility(y_max_length)
@@ -150,10 +153,10 @@ 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 Monotonic Alignment Search (MAS).
+            1. duration loss: loss between predicted token durations and those extracted by Monotinic Alignment Search (MAS).
             2. prior loss: loss between mel-spectrogram and encoder outputs.
             3. flow matching loss: loss between mel-spectrogram and decoder outputs.
 
@@ -176,15 +179,12 @@ class MatchaTTS(BaseLightningClass):  # 🍵
             spks = self.spk_emb(spks)
 
         # Get encoder_outputs `mu_x` and log-scaled token durations `logw`
-        mu_x, logw, x_mask = self.encoder(x, x_lengths, spks)
+        mu_x, enc_output, x_mask = self.encoder(x, x_lengths, spks)
         y_max_length = y.shape[-1]
 
         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:
-            attn = generate_path(durations.squeeze(1), attn_mask.squeeze(1))
-        else:
         # Use MAS to find most likely alignment `attn` between text and mel-spectrogram
         with torch.no_grad():
             const = -0.5 * math.log(2 * math.pi) * self.n_feats
@@ -195,12 +195,11 @@ class MatchaTTS(BaseLightningClass):  # 🍵
             log_prior = y_square - y_mu_double + mu_square + const
 
             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
         logw_ = torch.log(1e-8 + torch.sum(attn.unsqueeze(1), -1)) * x_mask
-        dur_loss = duration_loss(logw, logw_, x_lengths)
+        dur_loss = self.dp.compute_loss(logw_, enc_output, x_mask)
 
         # Cut a small segment of mel-spectrogram in order to increase batch size
         #   - "Hack" taken from Grad-TTS, in case of Grad-TTS, we cannot train batch size 32 on a 24GB GPU without it

matcha/text/__init__.py

@@ -1,5 +1,4 @@
 """ from https://github.com/keithito/tacotron """
-
 from matcha.text import cleaners
 from matcha.text.symbols import symbols
 
@@ -8,10 +7,6 @@ _symbol_to_id = {s: i for i, s in enumerate(symbols)}
 _id_to_symbol = {i: s for i, s in enumerate(symbols)}  # pylint: disable=unnecessary-comprehension
 
 
-class UnknownCleanerException(Exception):
-    pass
-
-
 def text_to_sequence(text, cleaner_names):
     """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
     Args:
@@ -53,6 +48,6 @@ def _clean_text(text, cleaner_names):
     for name in cleaner_names:
         cleaner = getattr(cleaners, name)
         if not cleaner:
-            raise UnknownCleanerException(f"Unknown cleaner: {name}")
+            raise Exception("Unknown cleaner: %s" % name)
         text = cleaner(text)
     return 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
@@ -36,12 +37,9 @@ global_phonemizer = phonemizer.backend.EspeakBackend(
 # Regular expression matching whitespace:
 _whitespace_re = re.compile(r"\s+")
 
-# Remove brackets
-_brackets_re = re.compile(r"[\[\]\(\)\{\}]")
-
 # List of (regular expression, replacement) pairs for abbreviations:
 _abbreviations = [
-    (re.compile(f"\\b{x[0]}\\.", re.IGNORECASE), x[1])
+    (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
     for x in [
         ("mrs", "misess"),
         ("mr", "mister"),
@@ -75,10 +73,6 @@ def lowercase(text):
     return text.lower()
 
 
-def remove_brackets(text):
-    return re.sub(_brackets_re, "", text)
-
-
 def collapse_whitespace(text):
     return re.sub(_whitespace_re, " ", text)
 
@@ -108,37 +102,15 @@ def english_cleaners2(text):
     text = lowercase(text)
     text = expand_abbreviations(text)
     phonemes = global_phonemizer.phonemize([text], strip=True, njobs=1)[0]
-    # Added in some cases espeak is not removing brackets
-    phonemes = remove_brackets(phonemes)
     phonemes = collapse_whitespace(phonemes)
     return phonemes
 
 
-def ipa_simplifier(text):
+def english_cleaners_piper(text):
-    replacements = [
+    """Pipeline for English text, including abbreviation expansion. + punctuation + stress"""
-        ("ɐ", "ə"),
+    text = convert_to_ascii(text)
-        ("ˈə", "ə"),
+    text = lowercase(text)
-        ("ʤ", "dʒ"),
+    text = expand_abbreviations(text)
-        ("ʧ", "tʃ"),
+    phonemes = "".join(piper_phonemize.phonemize_espeak(text=text, voice="en-US")[0])
-        ("ᵻ", "ɪ"),
+    phonemes = collapse_whitespace(phonemes)
-    ]
-    for replacement in replacements:
-        text = text.replace(replacement[0], replacement[1])
-    phonemes = collapse_whitespace(text)
     return phonemes
-
-
-# I am removing this due to incompatibility with several version of python
-# However, if you want to use it, you can uncomment it
-# and install piper-phonemize with the following command:
-# pip install piper-phonemize
-
-# import piper_phonemize
-# def english_cleaners_piper(text):
-#     """Pipeline for English text, including abbreviation expansion. + punctuation + stress"""
-#     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/text/symbols.py

@@ -2,7 +2,6 @@
 
 Defines the set of symbols used in text input to the model.
 """
-
 _pad = "_"
 _punctuation = ';:,.!?¡¿—…"«»“” '
 _letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"

matcha/utils/audio.py

@@ -48,7 +48,7 @@ def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin,
     if torch.max(y) > 1.0:
         print("max value is ", torch.max(y))
 
-    global mel_basis, hann_window  # pylint: disable=global-statement,global-variable-not-assigned
+    global mel_basis, hann_window  # pylint: disable=global-statement
     if f"{str(fmax)}_{str(y.device)}" not in mel_basis:
         mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
         mel_basis[str(fmax) + "_" + str(y.device)] = torch.from_numpy(mel).float().to(y.device)

matcha/utils/data/hificaptain.py

@@ -1,148 +0,0 @@
-#!/usr/bin/env python
-import argparse
-import os
-import sys
-import tempfile
-from pathlib import Path
-
-import torchaudio
-from torch.hub import download_url_to_file
-from tqdm import tqdm
-
-from matcha.utils.data.utils import _extract_zip
-
-URLS = {
-    "en-US": {
-        "female": "https://ast-astrec.nict.go.jp/release/hi-fi-captain/hfc_en-US_F.zip",
-        "male": "https://ast-astrec.nict.go.jp/release/hi-fi-captain/hfc_en-US_M.zip",
-    },
-    "ja-JP": {
-        "female": "https://ast-astrec.nict.go.jp/release/hi-fi-captain/hfc_ja-JP_F.zip",
-        "male": "https://ast-astrec.nict.go.jp/release/hi-fi-captain/hfc_ja-JP_M.zip",
-    },
-}
-
-INFO_PAGE = "https://ast-astrec.nict.go.jp/en/release/hi-fi-captain/"
-
-# On their website they say "We NICT open-sourced Hi-Fi-CAPTAIN",
-# but they use this very-much-not-open-source licence.
-# Dunno if this is open washing or stupidity.
-LICENCE = "CC BY-NC-SA 4.0"
-
-# I'd normally put the citation here. It's on their website.
-# Boo to non-open-source stuff.
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("-s", "--save-dir", type=str, default=None, help="Place to store the downloaded zip files")
-    parser.add_argument(
-        "-r",
-        "--skip-resampling",
-        action="store_true",
-        default=False,
-        help="Skip resampling the data (from 48 to 22.05)",
-    )
-    parser.add_argument(
-        "-l", "--language", type=str, choices=["en-US", "ja-JP"], default="en-US", help="The language to download"
-    )
-    parser.add_argument(
-        "-g",
-        "--gender",
-        type=str,
-        choices=["male", "female"],
-        default="female",
-        help="The gender of the speaker to download",
-    )
-    parser.add_argument(
-        "-o",
-        "--output_dir",
-        type=str,
-        default="data",
-        help="Place to store the converted data. Top-level only, the subdirectory will be created",
-    )
-
-    return parser.parse_args()
-
-
-def process_text(infile, outpath: Path):
-    outmode = "w"
-    if infile.endswith("dev.txt"):
-        outfile = outpath / "valid.txt"
-    elif infile.endswith("eval.txt"):
-        outfile = outpath / "test.txt"
-    else:
-        outfile = outpath / "train.txt"
-        if outfile.exists():
-            outmode = "a"
-    with (
-        open(infile, encoding="utf-8") as inf,
-        open(outfile, outmode, encoding="utf-8") as of,
-    ):
-        for line in inf.readlines():
-            line = line.strip()
-            fileid, rest = line.split(" ", maxsplit=1)
-            outfile = str(outpath / f"{fileid}.wav")
-            of.write(f"{outfile}|{rest}\n")
-
-
-def process_files(zipfile, outpath, resample=True):
-    with tempfile.TemporaryDirectory() as tmpdirname:
-        for filename in tqdm(_extract_zip(zipfile, tmpdirname)):
-            if not filename.startswith(tmpdirname):
-                filename = os.path.join(tmpdirname, filename)
-            if filename.endswith(".txt"):
-                process_text(filename, outpath)
-            elif filename.endswith(".wav"):
-                filepart = filename.rsplit("/", maxsplit=1)[-1]
-                outfile = str(outpath / filepart)
-                arr, sr = torchaudio.load(filename)
-                if resample:
-                    arr = torchaudio.functional.resample(arr, orig_freq=sr, new_freq=22050)
-                torchaudio.save(outfile, arr, 22050)
-            else:
-                continue
-
-
-def main():
-    args = get_args()
-
-    save_dir = None
-    if args.save_dir:
-        save_dir = Path(args.save_dir)
-        if not save_dir.is_dir():
-            save_dir.mkdir()
-
-    if not args.output_dir:
-        print("output directory not specified, exiting")
-        sys.exit(1)
-
-    URL = URLS[args.language][args.gender]
-    dirname = f"hi-fi_{args.language}_{args.gender}"
-
-    outbasepath = Path(args.output_dir)
-    if not outbasepath.is_dir():
-        outbasepath.mkdir()
-    outpath = outbasepath / dirname
-    if not outpath.is_dir():
-        outpath.mkdir()
-
-    resample = True
-    if args.skip_resampling:
-        resample = False
-
-    if save_dir:
-        zipname = URL.rsplit("/", maxsplit=1)[-1]
-        zipfile = save_dir / zipname
-        if not zipfile.exists():
-            download_url_to_file(URL, zipfile, progress=True)
-        process_files(zipfile, outpath, resample)
-    else:
-        with tempfile.NamedTemporaryFile(suffix=".zip", delete=True) as zf:
-            download_url_to_file(URL, zf.name, progress=True)
-            process_files(zf.name, outpath, resample)
-
-
-if __name__ == "__main__":
-    main()

matcha/utils/data/ljspeech.py

@@ -1,97 +0,0 @@
-#!/usr/bin/env python
-import argparse
-import random
-import tempfile
-from pathlib import Path
-
-from torch.hub import download_url_to_file
-
-from matcha.utils.data.utils import _extract_tar
-
-URL = "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2"
-
-INFO_PAGE = "https://keithito.com/LJ-Speech-Dataset/"
-
-LICENCE = "Public domain (LibriVox copyright disclaimer)"
-
-CITATION = """
-@misc{ljspeech17,
-  author       = {Keith Ito and Linda Johnson},
-  title        = {The LJ Speech Dataset},
-  howpublished = {\\url{https://keithito.com/LJ-Speech-Dataset/}},
-  year         = 2017
-}
-"""
-
-
-def decision():
-    return random.random() < 0.98
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("-s", "--save-dir", type=str, default=None, help="Place to store the downloaded zip files")
-    parser.add_argument(
-        "output_dir",
-        type=str,
-        nargs="?",
-        default="data",
-        help="Place to store the converted data (subdirectory LJSpeech-1.1 will be created)",
-    )
-
-    return parser.parse_args()
-
-
-def process_csv(ljpath: Path):
-    if (ljpath / "metadata.csv").exists():
-        basepath = ljpath
-    elif (ljpath / "LJSpeech-1.1" / "metadata.csv").exists():
-        basepath = ljpath / "LJSpeech-1.1"
-    csvpath = basepath / "metadata.csv"
-    wavpath = basepath / "wavs"
-
-    with (
-        open(csvpath, encoding="utf-8") as csvf,
-        open(basepath / "train.txt", "w", encoding="utf-8") as tf,
-        open(basepath / "val.txt", "w", encoding="utf-8") as vf,
-    ):
-        for line in csvf.readlines():
-            line = line.strip()
-            parts = line.split("|")
-            wavfile = str(wavpath / f"{parts[0]}.wav")
-            if decision():
-                tf.write(f"{wavfile}|{parts[1]}\n")
-            else:
-                vf.write(f"{wavfile}|{parts[1]}\n")
-
-
-def main():
-    args = get_args()
-
-    save_dir = None
-    if args.save_dir:
-        save_dir = Path(args.save_dir)
-        if not save_dir.is_dir():
-            save_dir.mkdir()
-
-    outpath = Path(args.output_dir)
-    if not outpath.is_dir():
-        outpath.mkdir()
-
-    if save_dir:
-        tarname = URL.rsplit("/", maxsplit=1)[-1]
-        tarfile = save_dir / tarname
-        if not tarfile.exists():
-            download_url_to_file(URL, str(tarfile), progress=True)
-        _extract_tar(tarfile, outpath)
-        process_csv(outpath)
-    else:
-        with tempfile.NamedTemporaryFile(suffix=".tar.bz2", delete=True) as zf:
-            download_url_to_file(URL, zf.name, progress=True)
-            _extract_tar(zf.name, outpath)
-            process_csv(outpath)
-
-
-if __name__ == "__main__":
-    main()

matcha/utils/data/utils.py

@@ -1,53 +0,0 @@
-# taken from https://github.com/pytorch/audio/blob/main/src/torchaudio/datasets/utils.py
-# Copyright (c) 2017 Facebook Inc. (Soumith Chintala)
-# Licence: BSD 2-Clause
-# pylint: disable=C0123
-
-import logging
-import os
-import tarfile
-import zipfile
-from pathlib import Path
-from typing import Any, List, Optional, Union
-
-_LG = logging.getLogger(__name__)
-
-
-def _extract_tar(from_path: Union[str, Path], to_path: Optional[str] = None, overwrite: bool = False) -> List[str]:
-    if type(from_path) is Path:
-        from_path = str(Path)
-
-    if to_path is None:
-        to_path = os.path.dirname(from_path)
-
-    with tarfile.open(from_path, "r") as tar:
-        files = []
-        for file_ in tar:  # type: Any
-            file_path = os.path.join(to_path, file_.name)
-            if file_.isfile():
-                files.append(file_path)
-                if os.path.exists(file_path):
-                    _LG.info("%s already extracted.", file_path)
-                    if not overwrite:
-                        continue
-            tar.extract(file_, to_path)
-        return files
-
-
-def _extract_zip(from_path: Union[str, Path], to_path: Optional[str] = None, overwrite: bool = False) -> List[str]:
-    if type(from_path) is Path:
-        from_path = str(Path)
-
-    if to_path is None:
-        to_path = os.path.dirname(from_path)
-
-    with zipfile.ZipFile(from_path, "r") as zfile:
-        files = zfile.namelist()
-        for file_ in files:
-            file_path = os.path.join(to_path, file_)
-            if os.path.exists(file_path):
-                _LG.info("%s already extracted.", file_path)
-                if not overwrite:
-                    continue
-            zfile.extract(file_, to_path)
-    return files

matcha/utils/generate_data_statistics.py

@@ -4,7 +4,6 @@ when needed.
 
 Parameters from hparam.py will be used
 """
-
 import argparse
 import json
 import os
@@ -95,7 +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()
@@ -103,8 +101,10 @@ def main():
     log.info("Dataloader loaded! Now computing stats...")
     params = compute_data_statistics(data_loader, cfg["n_feats"])
     print(params)
-    with open(output_file, "w", encoding="utf-8") as dumpfile:
+    json.dump(
-        json.dump(params, dumpfile)
+        params,
+        open(output_file, "w"),
+    )
 
 
 if __name__ == "__main__":

matcha/utils/get_durations_from_trained_model.py

@@ -4,7 +4,6 @@ when needed.
 
 Parameters from hparam.py will be used
 """
-
 import argparse
 import json
 import os
@@ -87,7 +86,7 @@ def main():
         "-i",
         "--input-config",
         type=str,
-        default="ljspeech.yaml",
+        default="vctk.yaml",
         help="The name of the yaml config file under configs/data",
     )
 
@@ -141,14 +140,11 @@ 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
 
     if args.output_folder is not None:
         output_folder = Path(args.output_folder)
     else:
-        output_folder = Path(cfg["train_filelist_path"]).parent / "durations"
+        output_folder = Path("data") / "processed_data" / cfg["name"] / "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")

matcha/utils/rich_utils.py

@@ -72,7 +72,7 @@ def print_config_tree(
 
     # save config tree to file
     if save_to_file:
-        with open(Path(cfg.paths.output_dir, "config_tree.log"), "w", encoding="utf-8") as file:
+        with open(Path(cfg.paths.output_dir, "config_tree.log"), "w") as file:
             rich.print(tree, file=file)
 
 
@@ -97,5 +97,5 @@ def enforce_tags(cfg: DictConfig, save_to_file: bool = False) -> None:
         log.info(f"Tags: {cfg.tags}")
 
     if save_to_file:
-        with open(Path(cfg.paths.output_dir, "tags.log"), "w", encoding="utf-8") as file:
+        with open(Path(cfg.paths.output_dir, "tags.log"), "w") as file:
             rich.print(cfg.tags, file=file)

requirements.txt

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

scripts/get_durations.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+
+echo "Starting script"
+
+echo "Getting LJ Speech durations"
+python matcha/utils/get_durations_from_trained_model.py -i ljspeech.yaml -c logs/train/lj_det/runs/2024-01-12_12-05-00/checkpoints/last.ckpt -f
+
+echo "Getting TSG2 durations"
+python matcha/utils/get_durations_from_trained_model.py -i tsg2.yaml -c logs/train/tsg2_det_dur/runs/2024-01-05_12-33-35/checkpoints/last.ckpt -f
+
+echo "Getting Joe Spont durations"
+python matcha/utils/get_durations_from_trained_model.py -i joe_spont_only.yaml -c logs/train/joe_det_dur/runs/2024-02-20_14-01-01/checkpoints/last.ckpt -f
+
+echo "Getting Ryan durations"
+python matcha/utils/get_durations_from_trained_model.py -i ryan.yaml -c logs/train/matcha_ryan_det/runs/2024-02-26_09-28-09/checkpoints/last.ckpt -f

scripts/transcribe.sh

@@ -0,0 +1,7 @@
+echo "Transcribing"
+
+whispertranscriber -i lj_det_output -o lj_det_output_transcriptions -f
+
+whispertranscriber -i lj_fm_output -o lj_fm_output_transcriptions -f
+wercompute -r dur_wer_computation/reference_transcripts/ -i lj_det_output_transcriptions
+wercompute -r dur_wer_computation/reference_transcripts/ -i lj_fm_output_transcriptions

scripts/wer_computer.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+# Run from root folder with: bash scripts/wer_computer.sh
+
+
+root_folder=${1:-"dur_wer_computation"}
+echo "Running WER computation for Duration predictors"
+cmd="wercompute -r ${root_folder}/reference_transcripts/ -i ${root_folder}/lj_fm_output_transcriptions/"
+# echo $cmd
+echo "LJ"
+echo "==================================="
+echo "Flow Matching"
+$cmd
+echo "-----------------------------------"
+
+echo "LJ Determinstic"
+cmd="wercompute -r ${root_folder}/reference_transcripts/ -i ${root_folder}/lj_det_output_transcriptions/"
+$cmd
+echo "-----------------------------------"
+
+echo "Cormac"
+echo "==================================="
+echo "Cormac Flow Matching"
+cmd="wercompute -r ${root_folder}/reference_transcripts/ -i ${root_folder}/fm_output_transcriptions/"
+$cmd
+echo "-----------------------------------"
+
+echo "Cormac Determinstic"
+cmd="wercompute -r ${root_folder}/reference_transcripts/ -i ${root_folder}/det_output_transcriptions/"
+$cmd
+echo "-----------------------------------"

setup.py

@@ -16,16 +16,9 @@ with open("README.md", encoding="utf-8") as readme_file:
     README = readme_file.read()
 
 cwd = os.path.dirname(os.path.abspath(__file__))
-with open(os.path.join(cwd, "matcha", "VERSION"), encoding="utf-8") as fin:
+with open(os.path.join(cwd, "matcha", "VERSION")) as fin:
     version = fin.read().strip()
 
-
-def get_requires():
-    requirements = os.path.join(os.path.dirname(__file__), "requirements.txt")
-    with open(requirements, encoding="utf-8") as reqfile:
-        return [str(r).strip() for r in reqfile]
-
-
 setup(
     name="matcha-tts",
     version=version,
@@ -35,7 +28,7 @@ setup(
     author="Shivam Mehta",
     author_email="shivam.mehta25@gmail.com",
     url="https://shivammehta25.github.io/Matcha-TTS",
-    install_requires=get_requires(),
+    install_requires=[str(r) for r in open(os.path.join(os.path.dirname(__file__), "requirements.txt"))],
     include_dirs=[numpy.get_include()],
     include_package_data=True,
     packages=find_packages(exclude=["tests", "tests/*", "examples", "examples/*"]),
@@ -45,7 +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),