Support different tts model types.

low (quality) -> runs faster. high (quality) -> runs slower

egs/ljspeech/TTS/prepare.sh

@@ -54,7 +54,7 @@ fi
 if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
   log "Stage 1: Prepare LJSpeech manifest"
   # We assume that you have downloaded the LJSpeech corpus
-  # to $dl_dir/LJSpeech
+  # to $dl_dir/LJSpeech-1.1
   mkdir -p data/manifests
   if [ ! -e data/manifests/.ljspeech.done ]; then
     lhotse prepare ljspeech $dl_dir/LJSpeech-1.1 data/manifests

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

@@ -25,9 +25,8 @@ Export the model to ONNX:
   --exp-dir vits/exp \
   --tokens data/tokens.txt
 
-It will generate two files inside vits/exp:
+It will generate one file inside vits/exp:
   - vits-epoch-1000.onnx
-  - vits-epoch-1000.int8.onnx (quantizated model)
 
 See ./test_onnx.py for how to use the exported ONNX models.
 """
@@ -40,7 +39,6 @@ from typing import Dict, Tuple
 import onnx
 import torch
 import torch.nn as nn
-from onnxruntime.quantization import QuantType, quantize_dynamic
 from tokenizer import Tokenizer
 from train import get_model, get_params
 
@@ -75,6 +73,15 @@ def get_parser():
         help="""Path to vocabulary.""",
     )
 
+    parser.add_argument(
+        "--model-type",
+        type=str,
+        default="",
+        help="""If not empty, valid values are: low, medium, high.
+        It controls the model size. low -> runs faster.
+        """,
+    )
+
     return parser
 
 
@@ -240,7 +247,7 @@ def main():
     model = OnnxModel(model=model)
 
     num_param = sum([p.numel() for p in model.parameters()])
-    logging.info(f"generator parameters: {num_param}")
+    logging.info(f"generator parameters: {num_param}, or {num_param/1000/1000} M")
 
     suffix = f"epoch-{params.epoch}"
 
@@ -256,18 +263,6 @@ def main():
     )
     logging.info(f"Exported generator to {model_filename}")
 
-    # Generate int8 quantization models
-    # See https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#data-type-selection
-
-    logging.info("Generate int8 quantization models")
-
-    model_filename_int8 = params.exp_dir / f"vits-{suffix}.int8.onnx"
-    quantize_dynamic(
-        model_input=model_filename,
-        model_output=model_filename_int8,
-        weight_type=QuantType.QUInt8,
-    )
-
 
 if __name__ == "__main__":
     formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"

egs/ljspeech/TTS/vits/generator.py

@@ -189,7 +189,7 @@ class VITSGenerator(torch.nn.Module):
         self.upsample_factor = int(np.prod(decoder_upsample_scales))
         self.spks = None
         if spks is not None and spks > 1:
-            assert global_channels > 0
+            assert global_channels > 0, global_channels
             self.spks = spks
             self.global_emb = torch.nn.Embedding(spks, global_channels)
         self.spk_embed_dim = None

egs/ljspeech/TTS/vits/infer.py

@@ -72,6 +72,15 @@ def get_parser():
         help="""Path to vocabulary.""",
     )
 
+    parser.add_argument(
+        "--model-type",
+        type=str,
+        default="",
+        help="""If not empty, valid values are: low, medium, high.
+        It controls the model size. low -> runs faster.
+        """,
+    )
+
     return parser
 
 
@@ -94,6 +103,7 @@ def infer_dataset(
       tokenizer:
         Used to convert text to phonemes.
     """
+
     #  Background worker save audios to disk.
     def _save_worker(
         batch_size: int,

egs/ljspeech/TTS/vits/test_model.py

@@ -0,0 +1,51 @@
+#!/usr/bin/env python3
+# Copyright    2024  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from tokenizer import Tokenizer
+from train import get_model, get_params
+from vits import VITS
+
+
+def test_model_type(model_type):
+    tokens = "./data/tokens.txt"
+
+    params = get_params()
+
+    tokenizer = Tokenizer(tokens)
+    params.blank_id = tokenizer.pad_id
+    params.vocab_size = tokenizer.vocab_size
+    params.model_type = model_type
+
+    model = get_model(params)
+    generator = model.generator
+
+    num_param = sum([p.numel() for p in generator.parameters()])
+    print(
+        f"{model_type}: generator parameters: {num_param}, or {num_param/1000/1000} M"
+    )
+
+
+def main():
+    test_model_type("high")  # 35.63 M
+    test_model_type("low")  # 7.55 M
+    test_model_type("medium")  # 23.61 M
+    test_model_type("")  # 35.63 M
+
+
+if __name__ == "__main__":
+    main()

egs/ljspeech/TTS/vits/text_encoder.py

@@ -92,9 +92,9 @@ class TextEncoder(torch.nn.Module):
             x_lengths (Tensor): Length tensor (B,).
 
         Returns:
-            Tensor: Encoded hidden representation (B, attention_dim, T_text).
+            Tensor: Encoded hidden representation (B, embed_dim, T_text).
-            Tensor: Projected mean tensor (B, attention_dim, T_text).
+            Tensor: Projected mean tensor (B, embed_dim, T_text).
-            Tensor: Projected scale tensor (B, attention_dim, T_text).
+            Tensor: Projected scale tensor (B, embed_dim, T_text).
             Tensor: Mask tensor for input tensor (B, 1, T_text).
 
         """
@@ -108,6 +108,7 @@ class TextEncoder(torch.nn.Module):
 
         # encoder assume the channel last (B, T_text, embed_dim)
         x = self.encoder(x, key_padding_mask=pad_mask)
+        # Note: attention_dim == embed_dim
 
         # convert the channel first (B, embed_dim, T_text)
         x = x.transpose(1, 2)

egs/ljspeech/TTS/vits/train.py

@@ -153,6 +153,15 @@ def get_parser():
         help="Whether to use half precision training.",
     )
 
+    parser.add_argument(
+        "--model-type",
+        type=str,
+        default="",
+        help="""If not empty, valid values are: low, medium, high.
+        It controls the model size. low -> runs faster.
+        """,
+    )
+
     return parser
 
 
@@ -189,15 +198,6 @@ def get_params() -> AttributeDict:
 
         - feature_dim: The model input dim. It has to match the one used
                        in computing features.
-
-        - subsampling_factor:  The subsampling factor for the model.
-
-        - encoder_dim: Hidden dim for multi-head attention model.
-
-        - num_decoder_layers: Number of decoder layer of transformer decoder.
-
-        - warm_step: The warmup period that dictates the decay of the
-              scale on "simple" (un-pruned) loss.
     """
     params = AttributeDict(
         {
@@ -278,6 +278,7 @@ def get_model(params: AttributeDict) -> nn.Module:
         vocab_size=params.vocab_size,
         feature_dim=params.feature_dim,
         sampling_rate=params.sampling_rate,
+        model_type=params.model_type,
         mel_loss_params=mel_loss_params,
         lambda_adv=params.lambda_adv,
         lambda_mel=params.lambda_mel,
@@ -363,7 +364,7 @@ def train_one_epoch(
     model.train()
     device = model.device if isinstance(model, DDP) else next(model.parameters()).device
 
-    # used to summary the stats over iterations in one epoch
+    # used to track the stats over iterations in one epoch
     tot_loss = MetricsTracker()
 
     saved_bad_model = False

egs/ljspeech/TTS/vits/vits.py

@@ -5,6 +5,7 @@
 
 """VITS module for GAN-TTS task."""
 
+import copy
 from typing import Any, Dict, Optional, Tuple
 
 import torch
@@ -38,6 +39,36 @@ AVAILABLE_DISCRIMINATORS = {
     "hifigan_multi_scale_multi_period_discriminator": HiFiGANMultiScaleMultiPeriodDiscriminator,  # NOQA
 }
 
+LOW_CONFIG = {
+    "hidden_channels": 96,
+    "decoder_upsample_scales": (8, 8, 4),
+    "decoder_channels": 256,
+    "decoder_upsample_kernel_sizes": (16, 16, 8),
+    "decoder_resblock_kernel_sizes": (3, 5, 7),
+    "decoder_resblock_dilations": ((1, 2), (2, 6), (3, 12)),
+    "text_encoder_cnn_module_kernel": 3,
+}
+
+MEDIUM_CONFIG = {
+    "hidden_channels": 192,
+    "decoder_upsample_scales": (8, 8, 4),
+    "decoder_channels": 256,
+    "decoder_upsample_kernel_sizes": (16, 16, 8),
+    "decoder_resblock_kernel_sizes": (3, 5, 7),
+    "decoder_resblock_dilations": ((1, 2), (2, 6), (3, 12)),
+    "text_encoder_cnn_module_kernel": 3,
+}
+
+HIGH_CONFIG = {
+    "hidden_channels": 192,
+    "decoder_upsample_scales": (8, 8, 2, 2),
+    "decoder_channels": 512,
+    "decoder_upsample_kernel_sizes": (16, 16, 4, 4),
+    "decoder_resblock_kernel_sizes": (3, 7, 11),
+    "decoder_resblock_dilations": ((1, 3, 5), (1, 3, 5), (1, 3, 5)),
+    "text_encoder_cnn_module_kernel": 3,
+}
+
 
 class VITS(nn.Module):
     """Implement VITS, `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`"""
@@ -49,6 +80,7 @@ class VITS(nn.Module):
         feature_dim: int = 513,
         sampling_rate: int = 22050,
         generator_type: str = "vits_generator",
+        model_type: str = "",
         generator_params: Dict[str, Any] = {
             "hidden_channels": 192,
             "spks": None,
@@ -155,12 +187,13 @@ class VITS(nn.Module):
         """Initialize VITS module.
 
         Args:
-            idim (int): Input vocabrary size.
+            idim (int): Input vocabulary size.
             odim (int): Acoustic feature dimension. The actual output channels will
                 be 1 since VITS is the end-to-end text-to-wave model but for the
                 compatibility odim is used to indicate the acoustic feature dimension.
             sampling_rate (int): Sampling rate, not used for the training but it will
                 be referred in saving waveform during the inference.
+            model_type (str): If not empty, must be one of: low, medium, high
             generator_type (str): Generator type.
             generator_params (Dict[str, Any]): Parameter dict for generator.
             discriminator_type (str): Discriminator type.
@@ -181,6 +214,24 @@ class VITS(nn.Module):
         """
         super().__init__()
 
+        generator_params = copy.deepcopy(generator_params)
+        discriminator_params = copy.deepcopy(discriminator_params)
+        generator_adv_loss_params = copy.deepcopy(generator_adv_loss_params)
+        discriminator_adv_loss_params = copy.deepcopy(discriminator_adv_loss_params)
+        feat_match_loss_params = copy.deepcopy(feat_match_loss_params)
+        mel_loss_params = copy.deepcopy(mel_loss_params)
+
+        if model_type != "":
+            assert model_type in ("low", "medium", "high"), model_type
+            if model_type == "low":
+                generator_params.update(LOW_CONFIG)
+            elif model_type == "medium":
+                generator_params.update(MEDIUM_CONFIG)
+            elif model_type == "high":
+                generator_params.update(HIGH_CONFIG)
+            else:
+                raise ValueError(f"Unknown model_type: ${model_type}")
+
         # define modules
         generator_class = AVAILABLE_GENERATERS[generator_type]
         if generator_type == "vits_generator":