Performed end to end testing on the matcha recipe (#1797)

* minor fixes to the `ljspeech/matcha` recipe

.github/scripts/ljspeech/TTS/run-matcha.sh

@@ -56,7 +56,7 @@ function infer() {
 
   curl -SL -O https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1
 
-  ./matcha/inference.py \
+  ./matcha/infer.py \
     --epoch 1 \
     --exp-dir ./matcha/exp \
     --tokens data/tokens.txt \

egs/ljspeech/TTS/README.md

@@ -131,12 +131,12 @@ To inference, use:
 
 wget https://github.com/csukuangfj/models/raw/refs/heads/master/hifigan/generator_v1
 
-./matcha/inference \
+./matcha/synth.py \
   --exp-dir ./matcha/exp-new-3 \
   --epoch 4000 \
   --tokens ./data/tokens.txt \
   --vocoder ./generator_v1 \
-  --input-text "how are you doing?"
+  --input-text "how are you doing?" \
   --output-wav ./generated.wav
 ```
 

egs/ljspeech/TTS/local/audio.py

@@ -0,0 +1 @@
+../matcha/audio.py

egs/ljspeech/TTS/local/compute_fbank_ljspeech.py

@@ -27,102 +27,17 @@
 import argparse
 import logging
 import os
-from dataclasses import dataclass
 from pathlib import Path
-from typing import Union
 
-import numpy as np
 import torch
+from fbank import MatchaFbank, MatchaFbankConfig
 from lhotse import CutSet, LilcomChunkyWriter, load_manifest
 from lhotse.audio import RecordingSet
-from lhotse.features.base import FeatureExtractor, register_extractor
 from lhotse.supervision import SupervisionSet
-from lhotse.utils import Seconds, compute_num_frames
-from matcha.audio import mel_spectrogram
 
 from icefall.utils import get_executor
 
 
-@dataclass
-class MyFbankConfig:
-    n_fft: int
-    n_mels: int
-    sampling_rate: int
-    hop_length: int
-    win_length: int
-    f_min: float
-    f_max: float
-
-
-@register_extractor
-class MyFbank(FeatureExtractor):
-
-    name = "MyFbank"
-    config_type = MyFbankConfig
-
-    def __init__(self, config):
-        super().__init__(config=config)
-
-    @property
-    def device(self) -> Union[str, torch.device]:
-        return self.config.device
-
-    def feature_dim(self, sampling_rate: int) -> int:
-        return self.config.n_mels
-
-    def extract(
-        self,
-        samples: np.ndarray,
-        sampling_rate: int,
-    ) -> torch.Tensor:
-        # Check for sampling rate compatibility.
-        expected_sr = self.config.sampling_rate
-        assert sampling_rate == expected_sr, (
-            f"Mismatched sampling rate: extractor expects {expected_sr}, "
-            f"got {sampling_rate}"
-        )
-        samples = torch.from_numpy(samples)
-        assert samples.ndim == 2, samples.shape
-        assert samples.shape[0] == 1, samples.shape
-
-        mel = (
-            mel_spectrogram(
-                samples,
-                self.config.n_fft,
-                self.config.n_mels,
-                self.config.sampling_rate,
-                self.config.hop_length,
-                self.config.win_length,
-                self.config.f_min,
-                self.config.f_max,
-                center=False,
-            )
-            .squeeze()
-            .t()
-        )
-
-        assert mel.ndim == 2, mel.shape
-        assert mel.shape[1] == self.config.n_mels, mel.shape
-
-        num_frames = compute_num_frames(
-            samples.shape[1] / sampling_rate, self.frame_shift, sampling_rate
-        )
-
-        if mel.shape[0] > num_frames:
-            mel = mel[:num_frames]
-        elif mel.shape[0] < num_frames:
-            mel = mel.unsqueeze(0)
-            mel = torch.nn.functional.pad(
-                mel, (0, 0, 0, num_frames - mel.shape[1]), mode="replicate"
-            ).squeeze(0)
-
-        return mel.numpy()
-
-    @property
-    def frame_shift(self) -> Seconds:
-        return self.config.hop_length / self.config.sampling_rate
-
-
 def get_parser():
     parser = argparse.ArgumentParser(
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -149,7 +64,7 @@ def compute_fbank_ljspeech(num_jobs: int):
     logging.info(f"num_jobs: {num_jobs}")
     logging.info(f"src_dir: {src_dir}")
     logging.info(f"output_dir: {output_dir}")
-    config = MyFbankConfig(
+    config = MatchaFbankConfig(
         n_fft=1024,
         n_mels=80,
         sampling_rate=22050,
@@ -170,7 +85,7 @@ def compute_fbank_ljspeech(num_jobs: int):
         src_dir / f"{prefix}_supervisions_{partition}.{suffix}", SupervisionSet
     )
 
-    extractor = MyFbank(config)
+    extractor = MatchaFbank(config)
 
     with get_executor() as ex:  # Initialize the executor only once.
         cuts_filename = f"{prefix}_cuts_{partition}.{suffix}"

egs/ljspeech/TTS/local/fbank.py

@@ -0,0 +1 @@
+../matcha/fbank.py

egs/ljspeech/TTS/matcha/export_onnx_hifigan.py

@@ -7,7 +7,7 @@
 
 import onnx
 import torch
-from inference import load_vocoder
+from infer import load_vocoder
 
 
 def add_meta_data(filename: str, meta_data: Dict[str, Any]):

egs/ljspeech/TTS/matcha/fbank.py

@@ -0,0 +1,88 @@
+from dataclasses import dataclass
+from typing import Union
+
+import numpy as np
+import torch
+from audio import mel_spectrogram
+from lhotse.features.base import FeatureExtractor, register_extractor
+from lhotse.utils import Seconds, compute_num_frames
+
+
+@dataclass
+class MatchaFbankConfig:
+    n_fft: int
+    n_mels: int
+    sampling_rate: int
+    hop_length: int
+    win_length: int
+    f_min: float
+    f_max: float
+
+
+@register_extractor
+class MatchaFbank(FeatureExtractor):
+
+    name = "MatchaFbank"
+    config_type = MatchaFbankConfig
+
+    def __init__(self, config):
+        super().__init__(config=config)
+
+    @property
+    def device(self) -> Union[str, torch.device]:
+        return self.config.device
+
+    def feature_dim(self, sampling_rate: int) -> int:
+        return self.config.n_mels
+
+    def extract(
+        self,
+        samples: np.ndarray,
+        sampling_rate: int,
+    ) -> torch.Tensor:
+        # Check for sampling rate compatibility.
+        expected_sr = self.config.sampling_rate
+        assert sampling_rate == expected_sr, (
+            f"Mismatched sampling rate: extractor expects {expected_sr}, "
+            f"got {sampling_rate}"
+        )
+        samples = torch.from_numpy(samples)
+        assert samples.ndim == 2, samples.shape
+        assert samples.shape[0] == 1, samples.shape
+
+        mel = (
+            mel_spectrogram(
+                samples,
+                self.config.n_fft,
+                self.config.n_mels,
+                self.config.sampling_rate,
+                self.config.hop_length,
+                self.config.win_length,
+                self.config.f_min,
+                self.config.f_max,
+                center=False,
+            )
+            .squeeze()
+            .t()
+        )
+
+        assert mel.ndim == 2, mel.shape
+        assert mel.shape[1] == self.config.n_mels, mel.shape
+
+        num_frames = compute_num_frames(
+            samples.shape[1] / sampling_rate, self.frame_shift, sampling_rate
+        )
+
+        if mel.shape[0] > num_frames:
+            mel = mel[:num_frames]
+        elif mel.shape[0] < num_frames:
+            mel = mel.unsqueeze(0)
+            mel = torch.nn.functional.pad(
+                mel, (0, 0, 0, num_frames - mel.shape[1]), mode="replicate"
+            ).squeeze(0)
+
+        return mel.numpy()
+
+    @property
+    def frame_shift(self) -> Seconds:
+        return self.config.hop_length / self.config.sampling_rate