minor updates

egs/vctk/TTS/local/prepare_token_file.py

@@ -17,19 +17,15 @@
 
 
 """
-This file reads the texts in given manifest and generate the file that maps tokens to IDs.
+This file reads the texts in given manifest and generates the file that maps tokens to IDs.
 """
 
 import argparse
 import logging
-from collections import Counter
 from pathlib import Path
 from typing import Dict
 
-import g2p_en
-import tacotron_cleaner.cleaners
 from lhotse import load_manifest
-from tqdm import tqdm
 
 
 def get_args():
@@ -74,35 +70,24 @@ def write_mapping(filename: str, sym2id: Dict[str, int]) -> None:
 
 def get_token2id(manifest_file: Path) -> Dict[str, int]:
     """Return a dict that maps token to IDs."""
-    extra_tokens = {
+    extra_tokens = [
-        "<blk>": 0,  # blank
+        "<blk>",  # 0 for blank
-        "<sos/eos>": 1,  # sos and eos symbols.
+        "<sos/eos>",  # 1 for sos and eos symbols.
-        "<unk>": 2,  # OOV
+        "<unk>",  # 2 for OOV
-    }
+    ]
-    cut_set = load_manifest(manifest_file)
+    all_tokens = set()
-    g2p = g2p_en.G2p()
-    counter = Counter()
 
-    for cut in tqdm(cut_set):
+    cut_set = load_manifest(manifest_file)
+
+    for cut in cut_set:
         # Each cut only contain one supervision
         assert len(cut.supervisions) == 1, len(cut.supervisions)
-        text = cut.supervisions[0].text
+        for t in cut.tokens:
-        # Text normalization
+            all_tokens.add(t)
-        text = tacotron_cleaner.cleaners.custom_english_cleaners(text)
-        # Convert to phonemes
-        tokens = g2p(text)
-        for t in tokens:
-            counter[t] += 1
 
-    # Sort by the number of occurrences in descending order
+    all_tokens = extra_tokens + list(all_tokens)
-    tokens_and_counts = sorted(counter.items(), key=lambda x: -x[1])
 
-    for token, idx in extra_tokens.items():
+    token2id: Dict[str, int] = {token: i for i, token in enumerate(all_tokens)}
-        tokens_and_counts.insert(idx, (token, None))
-
-    token2id: Dict[str, int] = {
-        token: i for i, (token, count) in enumerate(tokens_and_counts)
-    }
     return token2id
 
 

egs/vctk/TTS/local/prepare_tokens_vctk.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+# Copyright         2023  Xiaomi Corp.        (authors: Zengwei Yao,
+#                                                       Zengrui Jin,)
+#
+# 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.
+
+
+"""
+This file reads the texts in given manifest and save the new cuts with phoneme tokens.
+"""
+
+import logging
+from pathlib import Path
+
+import g2p_en
+import tacotron_cleaner.cleaners
+from lhotse import CutSet, load_manifest
+
+
+def prepare_tokens_vctk():
+    output_dir = Path("data/spectrogram")
+    prefix = "vctk"
+    suffix = "jsonl.gz"
+    partition = "all"
+
+    cut_set = load_manifest(output_dir / f"{prefix}_cuts_{partition}.{suffix}")
+    g2p = g2p_en.G2p()
+
+    new_cuts = []
+    for cut in cut_set:
+        # Each cut only contains one supervision
+        assert len(cut.supervisions) == 1, len(cut.supervisions)
+        text = cut.supervisions[0].normalized_text
+        # Text normalization
+        text = tacotron_cleaner.cleaners.custom_english_cleaners(text)
+        # Convert to phonemes
+        cut.tokens = g2p(text)
+        new_cuts.append(cut)
+
+    new_cut_set = CutSet.from_cuts(new_cuts)
+    new_cut_set.to_file(output_dir / f"{prefix}_cuts_with_tokens_{partition}.{suffix}")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    prepare_tokens_vctk()

egs/vctk/TTS/prepare.sh

@@ -66,7 +66,17 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
 fi
 
 if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
-  log "Stage 3: Split the VCTK cuts into train, valid and test sets"
+  log "Stage 3: Prepare phoneme tokens for VCTK"
+  if [ ! -e data/spectrogram/.vctk_with_token.done ]; then
+    ./local/prepare_tokens_vctk.py
+    mv data/spectrogram/vctk_cuts_with_tokens_all.jsonl.gz \
+      data/spectrogram/vctk_cuts_all.jsonl.gz
+    touch data/spectrogram/.vctk_with_token.done
+  fi
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 4: Split the VCTK cuts into train, valid and test sets"
   if [ ! -e data/spectrogram/.vctk_split.done ]; then
     lhotse subset --last 600 \
       data/spectrogram/vctk_cuts_all.jsonl.gz \
@@ -88,8 +98,12 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
   fi
 fi
 
-if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
-  log "Stage 4: Generate token file"
+  log "Stage 5: Generate token file"
+  # We assume you have installed g2p_en and espnet_tts_frontend.
+  # If not, please install them with:
+  #   - g2p_en: `pip install g2p_en`, refer to https://github.com/Kyubyong/g2p
+  #   - espnet_tts_frontend, `pip install espnet_tts_frontend`, refer to https://github.com/espnet/espnet_tts_frontend/
   if [ ! -e data/tokens.txt ]; then
     ./local/prepare_token_file.py \
       --manifest-file data/spectrogram/vctk_cuts_train.jsonl.gz \

egs/vctk/TTS/vits/duration_predictor.py

@@ -14,6 +14,7 @@ from typing import Optional
 
 import torch
 import torch.nn.functional as F
+
 from flow import (
     ConvFlow,
     DilatedDepthSeparableConv,

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

@@ -0,0 +1,267 @@
+#!/usr/bin/env python3
+#
+# Copyright      2023 Xiaomi Corporation     (Author: Zengwei Yao)
+#
+# 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.
+
+"""
+This script exports a VITS model from PyTorch to ONNX.
+
+Export the model to ONNX:
+./vits/export-onnx.py \
+  --epoch 1000 \
+  --exp-dir vits/exp \
+  --tokens data/tokens.txt
+
+It will generate two files 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.
+"""
+
+import argparse
+import logging
+from pathlib import Path
+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
+
+from icefall.checkpoint import load_checkpoint
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=1000,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="vits/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        default="data/tokens.txt",
+        help="""Path to vocabulary.""",
+    )
+
+    return parser
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, str]):
+    """Add meta data to an ONNX model. It is changed in-place.
+
+    Args:
+      filename:
+        Filename of the ONNX model to be changed.
+      meta_data:
+        Key-value pairs.
+    """
+    model = onnx.load(filename)
+    for key, value in meta_data.items():
+        meta = model.metadata_props.add()
+        meta.key = key
+        meta.value = value
+
+    onnx.save(model, filename)
+
+
+class OnnxModel(nn.Module):
+    """A wrapper for VITS generator."""
+
+    def __init__(self, model: nn.Module):
+        """
+        Args:
+          model:
+            A VITS generator.
+          frame_shift:
+            The frame shift in samples.
+        """
+        super().__init__()
+        self.model = model
+
+    def forward(
+        self,
+        tokens: torch.Tensor,
+        tokens_lens: torch.Tensor,
+        noise_scale: float = 0.667,
+        noise_scale_dur: float = 0.8,
+        alpha: float = 1.0,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Please see the help information of VITS.inference_batch
+
+        Args:
+          tokens:
+            Input text token indexes (1, T_text)
+          tokens_lens:
+            Number of tokens of shape (1,)
+          noise_scale (float):
+            Noise scale parameter for flow.
+          noise_scale_dur (float):
+            Noise scale parameter for duration predictor.
+          alpha (float):
+            Alpha parameter to control the speed of generated speech.
+
+        Returns:
+          Return a tuple containing:
+            - audio, generated wavform tensor, (B, T_wav)
+        """
+        audio, _, _ = self.model.inference(
+            text=tokens,
+            text_lengths=tokens_lens,
+            noise_scale=noise_scale,
+            noise_scale_dur=noise_scale_dur,
+            alpha=alpha,
+        )
+        return audio
+
+
+def export_model_onnx(
+    model: nn.Module,
+    model_filename: str,
+    opset_version: int = 11,
+) -> None:
+    """Export the given generator model to ONNX format.
+    The exported model has one input:
+
+        - tokens, a tensor of shape (1, T_text); dtype is torch.int64
+
+    and it has one output:
+
+        - audio, a tensor of shape (1, T'); dtype is torch.float32
+
+    Args:
+      model:
+        The VITS generator.
+      model_filename:
+        The filename to save the exported ONNX model.
+      opset_version:
+        The opset version to use.
+    """
+    tokens = torch.randint(low=0, high=79, size=(1, 13), dtype=torch.int64)
+    tokens_lens = torch.tensor([tokens.shape[1]], dtype=torch.int64)
+    noise_scale = torch.tensor([1], dtype=torch.float32)
+    noise_scale_dur = torch.tensor([1], dtype=torch.float32)
+    alpha = torch.tensor([1], dtype=torch.float32)
+
+    torch.onnx.export(
+        model,
+        (tokens, tokens_lens, noise_scale, noise_scale_dur, alpha),
+        model_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=[
+            "tokens",
+            "tokens_lens",
+            "noise_scale",
+            "noise_scale_dur",
+            "alpha",
+        ],
+        output_names=["audio"],
+        dynamic_axes={
+            "tokens": {0: "N", 1: "T"},
+            "tokens_lens": {0: "N"},
+            "audio": {0: "N", 1: "T"},
+        },
+    )
+
+    meta_data = {
+        "model_type": "VITS",
+        "version": "1",
+        "model_author": "k2-fsa",
+        "comment": "VITS generator",
+    }
+    logging.info(f"meta_data: {meta_data}")
+
+    add_meta_data(filename=model_filename, meta_data=meta_data)
+
+
+@torch.no_grad()
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    tokenizer = Tokenizer(params.tokens)
+    params.blank_id = tokenizer.blank_id
+    params.oov_id = tokenizer.oov_id
+    params.vocab_size = tokenizer.vocab_size
+
+    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 = model.generator
+    model.to("cpu")
+    model.eval()
+
+    model = OnnxModel(model=model)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"generator parameters: {num_param}")
+
+    suffix = f"epoch-{params.epoch}"
+
+    opset_version = 13
+
+    logging.info("Exporting encoder")
+    model_filename = params.exp_dir / f"vits-{suffix}.onnx"
+    export_model_onnx(
+        model,
+        model_filename,
+        opset_version=opset_version,
+    )
+    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"
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/vctk/TTS/vits/flow.py

@@ -13,6 +13,7 @@ import math
 from typing import Optional, Tuple, Union
 
 import torch
+
 from transform import piecewise_rational_quadratic_transform
 
 

egs/vctk/TTS/vits/generator.py

@@ -16,6 +16,9 @@ from typing import List, Optional, Tuple
 import numpy as np
 import torch
 import torch.nn.functional as F
+
+from icefall.utils import make_pad_mask
+
 from duration_predictor import StochasticDurationPredictor
 from hifigan import HiFiGANGenerator
 from posterior_encoder import PosteriorEncoder
@@ -23,8 +26,6 @@ from residual_coupling import ResidualAffineCouplingBlock
 from text_encoder import TextEncoder
 from utils import get_random_segments
 
-from icefall.utils import make_pad_mask
-
 
 class VITSGenerator(torch.nn.Module):
     """Generator module in VITS, `Conditional Variational Autoencoder
@@ -402,6 +403,7 @@ class VITSGenerator(torch.nn.Module):
         """
         # encoder
         x, m_p, logs_p, x_mask = self.text_encoder(text, text_lengths)
+        x_mask = x_mask.to(x.dtype)
         g = None
         if self.spks is not None:
             # (B, global_channels, 1)
@@ -479,6 +481,7 @@ class VITSGenerator(torch.nn.Module):
                 dur = torch.ceil(w)
             y_lengths = torch.clamp_min(torch.sum(dur, [1, 2]), 1).long()
             y_mask = (~make_pad_mask(y_lengths)).unsqueeze(1).to(text.device)
+            y_mask = y_mask.to(x.dtype)
             attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
             attn = self._generate_path(dur, attn_mask)
 

egs/vctk/TTS/vits/infer.py

@@ -38,7 +38,7 @@ import torch.nn as nn
 import torchaudio
 from tokenizer import Tokenizer
 from train import get_model, get_params
-from tts_datamodule import LJSpeechTtsDataModule
+from tts_datamodule import VctkTtsDataModule
 
 from icefall.checkpoint import load_checkpoint
 from icefall.utils import AttributeDict, setup_logger
@@ -94,6 +94,7 @@ def infer_dataset(
       tokenizer:
         Used to convert text to phonemes.
     """
+
     #  Background worker save audios to disk.
     def _save_worker(
         batch_size: int,
@@ -127,10 +128,10 @@ def infer_dataset(
     futures = []
     with ThreadPoolExecutor(max_workers=1) as executor:
         for batch_idx, batch in enumerate(dl):
-            batch_size = len(batch["text"])
+            batch_size = len(batch["tokens"])
 
-            text = batch["text"]
+            tokens = batch["tokens"]
-            tokens = tokenizer.texts_to_token_ids(text)
+            tokens = tokenizer.tokens_to_token_ids(tokens)
             tokens = k2.RaggedTensor(tokens)
             row_splits = tokens.shape.row_splits(1)
             tokens_lens = row_splits[1:] - row_splits[:-1]
@@ -180,7 +181,7 @@ def infer_dataset(
 @torch.no_grad()
 def main():
     parser = get_parser()
-    LJSpeechTtsDataModule.add_arguments(parser)
+    VctkTtsDataModule.add_arguments(parser)
     args = parser.parse_args()
     args.exp_dir = Path(args.exp_dir)
 
@@ -224,7 +225,7 @@ def main():
 
     # we need cut ids to display recognition results.
     args.return_cuts = True
-    ljspeech = LJSpeechTtsDataModule(args)
+    ljspeech = VctkTtsDataModule(args)
 
     test_cuts = ljspeech.test_cuts()
     test_dl = ljspeech.test_dataloaders(test_cuts)
@@ -236,6 +237,7 @@ def main():
         tokenizer=tokenizer,
     )
 
+    logging.info(f"Wav files are saved to {params.save_wav_dir}")
     logging.info("Done!")
 
 

egs/vctk/TTS/vits/loss.py

@@ -14,6 +14,7 @@ from typing import List, Tuple, Union
 import torch
 import torch.distributions as D
 import torch.nn.functional as F
+
 from lhotse.features.kaldi import Wav2LogFilterBank
 
 

egs/vctk/TTS/vits/posterior_encoder.py

@@ -12,9 +12,9 @@ This code is based on https://github.com/jaywalnut310/vits.
 from typing import Optional, Tuple
 
 import torch
-from wavenet import Conv1d, WaveNet
 
 from icefall.utils import make_pad_mask
+from wavenet import WaveNet, Conv1d
 
 
 class PosteriorEncoder(torch.nn.Module):

egs/vctk/TTS/vits/residual_coupling.py

@@ -12,6 +12,7 @@ This code is based on https://github.com/jaywalnut310/vits.
 from typing import Optional, Tuple, Union
 
 import torch
+
 from flow import FlipFlow
 from wavenet import WaveNet
 

egs/vctk/TTS/vits/test_onnx.py

@@ -0,0 +1,123 @@
+#!/usr/bin/env python3
+#
+# Copyright      2023 Xiaomi Corporation     (Author: Zengwei Yao)
+#
+# 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.
+
+"""
+This script is used to test the exported onnx model by vits/export-onnx.py
+
+Use the onnx model to generate a wav:
+./vits/test_onnx.py \
+  --model-filename vits/exp/vits-epoch-1000.onnx \
+  --tokens data/tokens.txt
+"""
+
+
+import argparse
+import logging
+import onnxruntime as ort
+import torch
+import torchaudio
+
+from tokenizer import Tokenizer
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--model-filename",
+        type=str,
+        required=True,
+        help="Path to the onnx model.",
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        default="data/tokens.txt",
+        help="""Path to vocabulary.""",
+    )
+
+    return parser
+
+
+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
+
+        self.session_opts = session_opts
+
+        self.model = ort.InferenceSession(
+            model_filename,
+            sess_options=self.session_opts,
+            providers=["CPUExecutionProvider"],
+        )
+        logging.info(f"{self.model.get_modelmeta().custom_metadata_map}")
+
+    def __call__(self, tokens: torch.Tensor, tokens_lens: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+          tokens:
+            A 1-D tensor of shape (1, T)
+        Returns:
+            A tensor of shape (1, T')
+        """
+        noise_scale = torch.tensor([0.667], dtype=torch.float32)
+        noise_scale_dur = torch.tensor([0.8], dtype=torch.float32)
+        alpha = torch.tensor([1.0], dtype=torch.float32)
+
+        out = self.model.run(
+            [
+                self.model.get_outputs()[0].name,
+            ],
+            {
+                self.model.get_inputs()[0].name: tokens.numpy(),
+                self.model.get_inputs()[1].name: tokens_lens.numpy(),
+                self.model.get_inputs()[2].name: noise_scale.numpy(),
+                self.model.get_inputs()[3].name: noise_scale_dur.numpy(),
+                self.model.get_inputs()[4].name: alpha.numpy(),
+            },
+        )[0]
+        return torch.from_numpy(out)
+
+
+def main():
+    args = get_parser().parse_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."
+    tokens = tokenizer.texts_to_token_ids([text])
+    tokens = torch.tensor(tokens)  # (1, T)
+    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)
+    logging.info("Saved to test_onnx.wav")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/vctk/TTS/vits/text_encoder.py

@@ -30,7 +30,7 @@ from typing import Optional, Tuple
 import torch
 from torch import Tensor, nn
 
-from icefall.utils import make_pad_mask
+from icefall.utils import is_jit_tracing, make_pad_mask
 
 
 class TextEncoder(torch.nn.Module):
@@ -442,18 +442,30 @@ class RelPositionMultiheadAttention(nn.Module):
         """
         (batch_size, num_heads, seq_len, n) = x.shape
 
-        assert n == 2 * seq_len - 1, f"{n} == 2 * {seq_len} - 1"
+        if not is_jit_tracing():
+            assert n == 2 * seq_len - 1, f"{n} == 2 * {seq_len} - 1"
 
-        # Note: TorchScript requires explicit arg for stride()
+        if is_jit_tracing():
-        batch_stride = x.stride(0)
+            rows = torch.arange(start=seq_len - 1, end=-1, step=-1)
-        head_stride = x.stride(1)
+            cols = torch.arange(seq_len)
-        time_stride = x.stride(2)
+            rows = rows.repeat(batch_size * num_heads).unsqueeze(-1)
-        n_stride = x.stride(3)
+            indexes = rows + cols
-        return x.as_strided(
+
-            (batch_size, num_heads, seq_len, seq_len),
+            x = x.reshape(-1, n)
-            (batch_stride, head_stride, time_stride - n_stride, n_stride),
+            x = torch.gather(x, dim=1, index=indexes)
-            storage_offset=n_stride * (seq_len - 1),
+            x = x.reshape(batch_size, num_heads, seq_len, seq_len)
-        )
+            return x
+        else:
+            # Note: TorchScript requires explicit arg for stride()
+            batch_stride = x.stride(0)
+            head_stride = x.stride(1)
+            time_stride = x.stride(2)
+            n_stride = x.stride(3)
+            return x.as_strided(
+                (batch_size, num_heads, seq_len, seq_len),
+                (batch_stride, head_stride, time_stride - n_stride, n_stride),
+                storage_offset=n_stride * (seq_len - 1),
+            )
 
     def forward(
         self,

egs/vctk/TTS/vits/tokenizer.py

@@ -77,3 +77,32 @@ class Tokenizer(object):
                 token_ids_list.append(token_ids)
 
         return token_ids_list
+
+    def tokens_to_token_ids(
+        self, tokens_list: List[str], intersperse_blank: bool = True
+    ):
+        """
+        Args:
+          tokens_list:
+            A list of token list, each corresponding to one utterance.
+          intersperse_blank:
+            Whether to intersperse blanks in the token sequence.
+
+        Returns:
+          Return a list of token id list [utterance][token_id]
+        """
+        token_ids_list = []
+
+        for tokens in tokens_list:
+            token_ids = []
+            for t in tokens:
+                if t in self.token2id:
+                    token_ids.append(self.token2id[t])
+                else:
+                    token_ids.append(self.oov_id)
+
+            if intersperse_blank:
+                token_ids = intersperse(token_ids, self.blank_id)
+                token_ids_list.append(token_ids)
+
+        return token_ids_list

egs/vctk/TTS/vits/train.py

@@ -34,7 +34,7 @@ from torch.cuda.amp import GradScaler, autocast
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import Optimizer
 from torch.utils.tensorboard import SummaryWriter
-from tts_datamodule import LJSpeechTtsDataModule
+from tts_datamodule import VctkTtsDataModule
 from utils import MetricsTracker, plot_feature, save_checkpoint
 from vits import VITS
 
@@ -294,10 +294,9 @@ def prepare_input(batch: dict, tokenizer: Tokenizer, device: torch.device):
     features = batch["features"].to(device)
     audio_lens = batch["audio_lens"].to(device)
     features_lens = batch["features_lens"].to(device)
-    text = batch["text"]
+    tokens = batch["tokens"]
-    speakers = batch["speakers"]
 
-    tokens = tokenizer.texts_to_token_ids(text)
+    tokens = tokenizer.tokens_to_token_ids(tokens)
     tokens = k2.RaggedTensor(tokens)
     row_splits = tokens.shape.row_splits(1)
     tokens_lens = row_splits[1:] - row_splits[:-1]
@@ -306,7 +305,7 @@ def prepare_input(batch: dict, tokenizer: Tokenizer, device: torch.device):
     # a tensor of shape (B, T)
     tokens = tokens.pad(mode="constant", padding_value=tokenizer.blank_id)
 
-    return audio, audio_lens, features, features_lens, tokens, tokens_lens, speakers
+    return audio, audio_lens, features, features_lens, tokens, tokens_lens
 
 
 def train_one_epoch(
@@ -384,16 +383,10 @@ def train_one_epoch(
     for batch_idx, batch in enumerate(train_dl):
         params.batch_idx_train += 1
 
-        batch_size = len(batch["text"])
+        batch_size = len(batch["tokens"])
-        (
+        audio, audio_lens, features, features_lens, tokens, tokens_lens = prepare_input(
-            audio,
+            batch, tokenizer, device
-            audio_lens,
+        )
-            features,
-            features_lens,
-            tokens,
-            tokens_lens,
-            speakers,
-        ) = prepare_input(batch, tokenizer, device)
 
         loss_info = MetricsTracker()
         loss_info["samples"] = batch_size
@@ -582,7 +575,7 @@ def compute_validation_loss(
 
     with torch.no_grad():
         for batch_idx, batch in enumerate(valid_dl):
-            batch_size = len(batch["text"])
+            batch_size = len(batch["tokens"])
             (
                 audio,
                 audio_lens,
@@ -810,7 +803,7 @@ def run(rank, world_size, args):
     if params.inf_check:
         register_inf_check_hooks(model)
 
-    ljspeech = LJSpeechTtsDataModule(args)
+    ljspeech = VctkTtsDataModule(args)
 
     train_cuts = ljspeech.train_cuts()
 
@@ -914,7 +907,7 @@ def run(rank, world_size, args):
 
 def main():
     parser = get_parser()
-    LJSpeechTtsDataModule.add_arguments(parser)
+    VctkTtsDataModule.add_arguments(parser)
     args = parser.parse_args()
     args.exp_dir = Path(args.exp_dir)
 

egs/vctk/TTS/vits/tts_datamodule.py

@@ -52,7 +52,7 @@ class _SeedWorkers:
         fix_random_seed(self.seed + worker_id)
 
 
-class LJSpeechTtsDataModule:
+class VctkTtsDataModule:
     """
     DataModule for tts experiments.
     It assumes there is always one train and valid dataloader,
@@ -168,7 +168,8 @@ class LJSpeechTtsDataModule:
         """
         logging.info("About to create train dataset")
         train = SpeechSynthesisDataset(
-            return_tokens=False,
+            return_text=False,
+            return_tokens=True,
             feature_input_strategy=eval(self.args.input_strategy)(),
             return_cuts=self.args.return_cuts,
         )
@@ -182,7 +183,8 @@ class LJSpeechTtsDataModule:
                 use_fft_mag=True,
             )
             train = SpeechSynthesisDataset(
-                return_tokens=False,
+                return_text=False,
+                return_tokens=True,
                 feature_input_strategy=OnTheFlyFeatures(Spectrogram(config)),
                 return_cuts=self.args.return_cuts,
             )
@@ -236,13 +238,15 @@ class LJSpeechTtsDataModule:
                 use_fft_mag=True,
             )
             validate = SpeechSynthesisDataset(
-                return_tokens=False,
+                return_text=False,
+                return_tokens=True,
                 feature_input_strategy=OnTheFlyFeatures(Spectrogram(config)),
                 return_cuts=self.args.return_cuts,
             )
         else:
             validate = SpeechSynthesisDataset(
-                return_tokens=False,
+                return_text=False,
+                return_tokens=True,
                 feature_input_strategy=eval(self.args.input_strategy)(),
                 return_cuts=self.args.return_cuts,
             )
@@ -273,13 +277,15 @@ class LJSpeechTtsDataModule:
                 use_fft_mag=True,
             )
             test = SpeechSynthesisDataset(
-                return_tokens=False,
+                return_text=False,
+                return_tokens=True,
                 feature_input_strategy=OnTheFlyFeatures(Spectrogram(config)),
                 return_cuts=self.args.return_cuts,
             )
         else:
             test = SpeechSynthesisDataset(
-                return_tokens=False,
+                return_text=False,
+                return_tokens=True,
                 feature_input_strategy=eval(self.args.input_strategy)(),
                 return_cuts=self.args.return_cuts,
             )

egs/vctk/TTS/vits/utils.py

@@ -14,15 +14,15 @@
 # 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 typing import Any, Dict, List, Optional, Tuple, Union
 import collections
 import logging
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
-import torch.distributed as dist
 import torch.nn as nn
+import torch.distributed as dist
 from lhotse.dataset.sampling.base import CutSampler
+from pathlib import Path
 from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import Optimizer

egs/vctk/TTS/vits/vits.py

@@ -9,7 +9,8 @@ from typing import Any, Dict, Optional, Tuple
 
 import torch
 import torch.nn as nn
-from generator import VITSGenerator
+from torch.cuda.amp import autocast
+
 from hifigan import (
     HiFiGANMultiPeriodDiscriminator,
     HiFiGANMultiScaleDiscriminator,
@@ -24,8 +25,9 @@ from loss import (
     KLDivergenceLoss,
     MelSpectrogramLoss,
 )
-from torch.cuda.amp import autocast
 from utils import get_segments
+from generator import VITSGenerator
+
 
 AVAILABLE_GENERATERS = {
     "vits_generator": VITSGenerator,
@@ -570,6 +572,7 @@ class VITS(nn.Module):
         self,
         text: torch.Tensor,
         text_lengths: torch.Tensor,
+        sids: Optional[torch.Tensor] = None,
         durations: Optional[torch.Tensor] = None,
         noise_scale: float = 0.667,
         noise_scale_dur: float = 0.8,
@@ -582,6 +585,7 @@ class VITS(nn.Module):
         Args:
             text (Tensor): Input text index tensor (B, T_text).
             text_lengths (Tensor): Input text index tensor (B,).
+            sids (Tensor): Speaker index tensor (B,).
             noise_scale (float): Noise scale value for flow.
             noise_scale_dur (float): Noise scale value for duration predictor.
             alpha (float): Alpha parameter to control the speed of generated speech.
@@ -596,6 +600,7 @@ class VITS(nn.Module):
         wav, att_w, dur = self.generator.inference(
             text=text,
             text_lengths=text_lengths,
+            sids=sids,
             noise_scale=noise_scale,
             noise_scale_dur=noise_scale_dur,
             alpha=alpha,

egs/vctk/TTS/vits/wavenet.py

@@ -9,8 +9,9 @@ This code is modified from https://github.com/kan-bayashi/ParallelWaveGAN.
 
 """
 
-import logging
 import math
+import logging
+
 from typing import Optional, Tuple
 
 import torch