Merge pull request #42 from shivammehta25/dev

Merging dev adding another dataset, piper phonemizer and refractoring

configs/data/hi-fi_en-US_female.yaml

@@ -0,0 +1,14 @@
+defaults:
+  - ljspeech
+  - _self_
+
+# 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/filelists/hi-fi-captain-en-us-female_train.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
+  mel_mean: -6.38385
+  mel_std: 2.541796

configs/experiment/hifi_dataset_piper_phonemizer.yaml

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

configs/model/matcha.yaml

@@ -12,3 +12,4 @@ spk_emb_dim: 64
 n_feats: 80
 data_statistics: ${data.data_statistics}
 out_size: null # Must be divisible by 4
+prior_loss: true

matcha/VERSION

@@ -1 +1 @@
-0.0.4
+0.0.5

matcha/models/baselightningmodule.py

@@ -81,7 +81,7 @@ def training_step(self, batch: Any, batch_idx: int):
             "step",
             float(self.global_step),
             on_step=True,
-            on_epoch=True,
+            prog_bar=True,
             logger=True,
             sync_dist=True,
         )

matcha/models/components/flow_matching.py

@@ -73,16 +73,14 @@ def solve_euler(self, x, t_span, mu, mask, spks, cond):
         # Or in future might add like a return_all_steps flag
         sol = []
 
-        steps = 1
-        while steps <= len(t_span) - 1:
+        for step in range(1, len(t_span)):
             dphi_dt = self.estimator(x, mask, mu, t, spks, cond)
 
             x = x + dt * dphi_dt
             t = t + dt
             sol.append(x)
-            if steps < len(t_span) - 1:
-                dt = t_span[steps + 1] - t
-            steps += 1
+            if step < len(t_span) - 1:
+                dt = t_span[step + 1] - t
 
         return sol[-1]
 

matcha/models/matcha_tts.py

@@ -34,6 +34,7 @@ def __init__(
         out_size,
         optimizer=None,
         scheduler=None,
+        prior_loss=True,
     ):
         super().__init__()
 
@@ -44,6 +45,7 @@ def __init__(
         self.spk_emb_dim = spk_emb_dim
         self.n_feats = n_feats
         self.out_size = out_size
+        self.prior_loss = prior_loss
 
         if n_spks > 1:
             self.spk_emb = torch.nn.Embedding(n_spks, spk_emb_dim)
@@ -228,7 +230,10 @@ def forward(self, x, x_lengths, y, y_lengths, spks=None, out_size=None, cond=Non
         # Compute loss of the decoder
         diff_loss, _ = self.decoder.compute_loss(x1=y, mask=y_mask, mu=mu_y, spks=spks, cond=cond)
 
-        prior_loss = torch.sum(0.5 * ((y - mu_y) ** 2 + math.log(2 * math.pi)) * y_mask)
-        prior_loss = prior_loss / (torch.sum(y_mask) * self.n_feats)
+        if self.prior_loss:
+            prior_loss = torch.sum(0.5 * ((y - mu_y) ** 2 + math.log(2 * math.pi)) * y_mask)
+            prior_loss = prior_loss / (torch.sum(y_mask) * self.n_feats)
+        else:
+            prior_loss = 0
 
         return dur_loss, prior_loss, diff_loss

matcha/text/cleaners.py

@@ -15,6 +15,7 @@
 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
@@ -103,3 +104,13 @@ def english_cleaners2(text):
     phonemes = global_phonemizer.phonemize([text], strip=True, njobs=1)[0]
     phonemes = collapse_whitespace(phonemes)
     return phonemes
+
+
+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

requirements.txt

@@ -35,10 +35,11 @@ torchaudio
 matplotlib
 pandas
 conformer==0.3.2
-diffusers==0.21.3
+diffusers==0.25.0
 notebook
 ipywidgets
 gradio
 gdown
 wget
 seaborn
+piper_phonemize