minor adjustment

.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/synth.py \
+  ./matcha/infer.py \
     --epoch 1 \
     --exp-dir ./matcha/exp \
     --tokens data/tokens.txt \

egs/ljspeech/TTS/matcha/infer.py

@@ -65,6 +65,28 @@ def get_parser():
         help="""Path to vocabulary.""",
     )
 
+    # The following arguments are used for inference on single text
+    parser.add_argument(
+        "--input-text",
+        type=str,
+        required=False,
+        help="The text to generate speech for",
+    )
+
+    parser.add_argument(
+        "--output-wav",
+        type=str,
+        required=False,
+        help="The filename of the wave to save the generated speech",
+    )
+
+    parser.add_argument(
+        "--sampling-rate",
+        type=int,
+        default=22050,
+        help="The sampling rate of the generated speech (default: 22050 for LJSpeech)",
+    )
+
     return parser
 
 
@@ -103,7 +125,7 @@ def process_text(text: str, tokenizer: Tokenizer, device: str = "cpu") -> dict:
     return {"x_orig": text, "x": x, "x_lengths": x_lengths}
 
 
-def synthesise(
+def synthesize(
     model: nn.Module,
     tokenizer: Tokenizer,
     n_timesteps: int,
@@ -169,7 +191,7 @@ def infer_dataset(
         cut_ids = [cut.id for cut in batch["cut"]]
 
         for i in range(batch_size):
-            output = synthesise(
+            output = synthesize(
                 model=model,
                 tokenizer=tokenizer,
                 n_timesteps=params.n_timesteps,
@@ -271,15 +293,35 @@ def main():
     denoiser = Denoiser(vocoder, mode="zeros")
     denoiser.to(device)
 
-    infer_dataset(
+    if params.input_text is not None and params.output_wav is not None:
-        dl=test_dl,
+        logging.info("Synthesizing a single text")
-        params=params,
+        output = synthesize(
-        model=model,
+            model=model,
-        vocoder=vocoder,
+            tokenizer=tokenizer,
-        denoiser=denoiser,
+            n_timesteps=params.n_timesteps,
-        tokenizer=tokenizer,
+            text=params.input_text,
-    )
+            length_scale=params.length_scale,
+            temperature=params.temperature,
+            device=device,
+        )
+        output["waveform"] = to_waveform(output["mel"], vocoder, denoiser)
 
+        sf.write(
+            file=params.output_wav,
+            data=output["waveform"],
+            samplerate=params.sampling_rate,
+            subtype="PCM_16",
+        )
+    else:
+        logging.info("Decoding the test set")
+        infer_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            vocoder=vocoder,
+            denoiser=denoiser,
+            tokenizer=tokenizer,
+        )
 
 if __name__ == "__main__":
     main()

egs/ljspeech/TTS/matcha/synth.py

@@ -1,166 +0,0 @@
-#!/usr/bin/env python3
-# Copyright         2024  Xiaomi Corp.        (authors: Fangjun Kuang)
-
-import argparse
-import json
-import logging
-from pathlib import Path
-
-import soundfile as sf
-import torch
-from hifigan.denoiser import Denoiser
-from infer import load_vocoder, synthesise, to_waveform
-from tokenizer import Tokenizer
-from train import get_model, get_params
-
-from icefall.checkpoint import load_checkpoint
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--epoch",
-        type=int,
-        default=4000,
-        help="""It specifies the checkpoint to use for decoding.
-        Note: Epoch counts from 1.
-        """,
-    )
-
-    parser.add_argument(
-        "--exp-dir",
-        type=Path,
-        default="matcha/exp",
-        help="""The experiment dir.
-        It specifies the directory where all training related
-        files, e.g., checkpoints, log, etc, are saved
-        """,
-    )
-
-    parser.add_argument(
-        "--vocoder",
-        type=Path,
-        default="./generator_v1",
-        help="Path to the vocoder",
-    )
-
-    parser.add_argument(
-        "--tokens",
-        type=Path,
-        default="data/tokens.txt",
-    )
-
-    parser.add_argument(
-        "--cmvn",
-        type=str,
-        default="data/fbank/cmvn.json",
-        help="""Path to vocabulary.""",
-    )
-
-    parser.add_argument(
-        "--input-text",
-        type=str,
-        required=True,
-        help="The text to generate speech for",
-    )
-
-    parser.add_argument(
-        "--output-wav",
-        type=str,
-        required=True,
-        help="The filename of the wave to save the generated speech",
-    )
-
-    parser.add_argument(
-        "--sampling-rate",
-        type=int,
-        default=22050,
-        help="The sampling rate of the generated speech (default: 22050 for LJSpeech)",
-    )
-
-    return parser
-
-
-@torch.inference_mode()
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-    params = get_params()
-
-    params.update(vars(args))
-
-    logging.info("Infer started")
-
-    device = torch.device("cpu")
-    if torch.cuda.is_available():
-        device = torch.device("cuda", 0)
-
-    tokenizer = Tokenizer(params.tokens)
-    params.blank_id = tokenizer.pad_id
-    params.vocab_size = tokenizer.vocab_size
-    params.model_args.n_vocab = params.vocab_size
-
-    with open(params.cmvn) as f:
-        stats = json.load(f)
-        params.data_args.data_statistics.mel_mean = stats["fbank_mean"]
-        params.data_args.data_statistics.mel_std = stats["fbank_std"]
-
-        params.model_args.data_statistics.mel_mean = stats["fbank_mean"]
-        params.model_args.data_statistics.mel_std = stats["fbank_std"]
-
-    # Number of ODE Solver steps
-    params.n_timesteps = 2
-
-    # Changes to the speaking rate
-    params.length_scale = 1.0
-
-    # Sampling temperature
-    params.temperature = 0.667
-    logging.info(params)
-
-    logging.info("About to create model")
-    model = get_model(params)
-
-    load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
-    model.to(device)
-    model.eval()
-
-    if not Path(params.vocoder).is_file():
-        raise ValueError(f"{params.vocoder} does not exist")
-
-    vocoder = load_vocoder(params.vocoder)
-    vocoder.to(device)
-
-    denoiser = Denoiser(vocoder, mode="zeros")
-    denoiser.to(device)
-
-    output = synthesise(
-        model=model,
-        tokenizer=tokenizer,
-        n_timesteps=params.n_timesteps,
-        text=params.input_text,
-        length_scale=params.length_scale,
-        temperature=params.temperature,
-        device=device,
-    )
-    output["waveform"] = to_waveform(output["mel"], vocoder, denoiser)
-
-    sf.write(
-        file=params.output_wav,
-        data=output["waveform"],
-        samplerate=params.sampling_rate,
-        subtype="PCM_16",
-    )
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-
-    torch.set_num_threads(1)
-    torch.set_num_interop_threads(1)
-    main()