Update README

egs/ljspeech/TTS/README.md

@@ -36,3 +36,19 @@ To inference, use:
   --epoch 1000 \
   --tokens data/tokens.txt
 ```
+
+## Quality vs speed
+
+If you feel that the trained model is slow at runtime, you can specify the
+argument `--model-type` during training. Possible values are:
+
+  - `low`, means **low** quality. The resulting model is very small in file size
+    and runs very fast. The following is a wave file generatd by a `low` model
+
+    https://github.com/k2-fsa/icefall/assets/5284924/d5758c24-470d-40ee-b089-e57fcba81633
+
+    The text is "Ask not what your country can do for you; ask what you can do for your country."
+
+  - `medium`, means **medium** quality.
+  - `high`, means **high** quality
+

egs/ljspeech/TTS/vits/export-onnx.py

@@ -143,7 +143,7 @@ class OnnxModel(nn.Module):
           Return a tuple containing:
             - audio, generated wavform tensor, (B, T_wav)
         """
-        audio, _, _ = self.model.inference(
+        audio, _, _ = self.model.generator.inference(
             text=tokens,
             text_lengths=tokens_lens,
             noise_scale=noise_scale,
@@ -205,6 +205,11 @@ def export_model_onnx(
         },
     )
 
+    if model.model.spks is None:
+        num_speakers = 1
+    else:
+        num_speakers = model.model.spks
+
     meta_data = {
         "model_type": "vits",
         "version": "1",
@@ -213,8 +218,8 @@ def export_model_onnx(
         "language": "English",
         "voice": "en-us",  # Choose your language appropriately
         "has_espeak": 1,
-        "n_speakers": 1,
+        "n_speakers": num_speakers,
-        "sample_rate": 22050,  # Must match the real sample rate
+        "sample_rate": model.model.sampling_rate,  # Must match the real sample rate
     }
     logging.info(f"meta_data: {meta_data}")
 
@@ -240,7 +245,6 @@ def main():
 
     load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
 
-    model = model.generator
     model.to("cpu")
     model.eval()
 

egs/ljspeech/TTS/vits/test_onnx.py

@@ -54,6 +54,20 @@ def get_parser():
         help="""Path to vocabulary.""",
     )
 
+    parser.add_argument(
+        "--text",
+        type=str,
+        default="Ask not what your country can do for you; ask what you can do for your country.",
+        help="Text to generate speech for",
+    )
+
+    parser.add_argument(
+        "--output-filename",
+        type=str,
+        default="test_onnx.wav",
+        help="Filename to save the generated wave file.",
+    )
+
     return parser
 
 
@@ -61,7 +75,7 @@ class OnnxModel:
     def __init__(self, model_filename: str):
         session_opts = ort.SessionOptions()
         session_opts.inter_op_num_threads = 1
-        session_opts.intra_op_num_threads = 4
+        session_opts.intra_op_num_threads = 1
 
         self.session_opts = session_opts
 
@@ -72,6 +86,9 @@ class OnnxModel:
         )
         logging.info(f"{self.model.get_modelmeta().custom_metadata_map}")
 
+        metadata = self.model.get_modelmeta().custom_metadata_map
+        self.sample_rate = int(metadata["sample_rate"])
+
     def __call__(self, tokens: torch.Tensor, tokens_lens: torch.Tensor) -> torch.Tensor:
         """
         Args:
@@ -101,13 +118,14 @@ class OnnxModel:
 
 def main():
     args = get_parser().parse_args()
+    logging.info(vars(args))
 
     tokenizer = Tokenizer(args.tokens)
 
     logging.info("About to create onnx model")
     model = OnnxModel(args.model_filename)
 
-    text = "I went there to see the land, the people and how their system works, end quote."
+    text = args.text
     tokens = tokenizer.texts_to_token_ids(
         [text], intersperse_blank=True, add_sos=True, add_eos=True
     )
@@ -115,8 +133,9 @@ def main():
     tokens_lens = torch.tensor([tokens.shape[1]], dtype=torch.int64)  # (1, T)
     audio = model(tokens, tokens_lens)  # (1, T')
 
-    torchaudio.save(str("test_onnx.wav"), audio, sample_rate=22050)
+    output_filename = args.output_filename
-    logging.info("Saved to test_onnx.wav")
+    torchaudio.save(output_filename, audio, sample_rate=model.sample_rate)
+    logging.info(f"Saved to {output_filename}")
 
 
 if __name__ == "__main__":

egs/ljspeech/TTS/vits/tokenizer.py

@@ -18,7 +18,15 @@ import logging
 from typing import Dict, List
 
 import tacotron_cleaner.cleaners
-from piper_phonemize import phonemize_espeak
+
+try:
+    from piper_phonemize import phonemize_espeak
+except Exception as ex:
+    raise RuntimeError(
+        f"{ex}\nPlease follow instructions in "
+        "../prepare.sh to install piper-phonemize"
+    )
+
 from utils import intersperse