A TTS recipe VITS on VCTK dataset (#1380)

* init

* isort formatted

* minor updates

* Create shared

* Update prepare_tokens_vctk.py

* Update prepare_tokens_vctk.py

* Update prepare_tokens_vctk.py

* Update prepare.sh

* updated

* Update train.py

* Update train.py

* Update tts_datamodule.py

* Update train.py

* Update train.py

* Update train.py

* Update train.py

* Update train.py

* Update train.py

* fixed formatting issue

* Update infer.py

* removed redundant files

* Create monotonic_align

* removed redundant files

* created symlinks

* Update prepare.sh

* minor adjustments

* Create requirements_tts.txt

* Update requirements_tts.txt

added version constraints

* Update infer.py

* Update infer.py

* Update infer.py

* updated docs

* Update export-onnx.py

* Update export-onnx.py

* Update test_onnx.py

* updated requirements.txt

* Update test_onnx.py

* Update test_onnx.py

* docs updated

* docs fixed

* minor updates

docs/source/recipes/TTS/index.rst

@@ -5,3 +5,4 @@ TTS
    :maxdepth: 2
 
    ljspeech/vits
+   vctk/vits

docs/source/recipes/TTS/ljspeech/vits.rst

@@ -4,6 +4,10 @@ VITS
 This tutorial shows you how to train an VITS model
 with the `LJSpeech <https://keithito.com/LJ-Speech-Dataset/>`_ dataset.
 
+.. note::
+  
+   TTS related recipes require packages in ``requirements-tts.txt``.
+
 .. note::
 
    The VITS paper: `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech <https://arxiv.org/pdf/2106.06103.pdf>`_
@@ -27,6 +31,12 @@ To run stage 1 to stage 5, use
 Build Monotonic Alignment Search
 --------------------------------
 
+.. code-block:: bash
+
+  $ ./prepare.sh --stage -1 --stop_stage -1
+
+or
+
 .. code-block:: bash
 
   $ cd vits/monotonic_align
@@ -74,7 +84,7 @@ training part first. It will save the ground-truth and generated wavs to the dir
   $ ./vits/infer.py \
       --epoch 1000 \
       --exp-dir vits/exp \
-      --tokens data/tokens.txt
+      --tokens data/tokens.txt \
       --max-duration 500
 
 .. note::

docs/source/recipes/TTS/vctk/vits.rst

@@ -0,0 +1,125 @@
+VITS
+===============
+
+This tutorial shows you how to train an VITS model
+with the `VCTK <https://datashare.ed.ac.uk/handle/10283/3443>`_ dataset.
+
+.. note::
+  
+   TTS related recipes require packages in ``requirements-tts.txt``.
+
+.. note::
+
+   The VITS paper: `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech <https://arxiv.org/pdf/2106.06103.pdf>`_
+
+
+Data preparation
+----------------
+
+.. code-block:: bash
+
+  $ cd egs/vctk/TTS
+  $ ./prepare.sh
+
+To run stage 1 to stage 6, use
+
+.. code-block:: bash
+
+  $ ./prepare.sh --stage 1 --stop_stage 6
+
+
+Build Monotonic Alignment Search
+--------------------------------
+
+To build the monotonic alignment search, use the following commands:
+
+.. code-block:: bash
+
+  $ ./prepare.sh --stage -1 --stop_stage -1
+
+or
+
+.. code-block:: bash
+
+  $ cd vits/monotonic_align
+  $ python setup.py build_ext --inplace
+  $ cd ../../
+
+
+Training
+--------
+
+.. code-block:: bash
+
+  $ export CUDA_VISIBLE_DEVICES="0,1,2,3"
+  $ ./vits/train.py \
+      --world-size 4 \
+      --num-epochs 1000 \
+      --start-epoch 1 \
+      --use-fp16 1 \
+      --exp-dir vits/exp \
+      --tokens data/tokens.txt
+      --max-duration 350
+
+.. note::
+
+    You can adjust the hyper-parameters to control the size of the VITS model and
+    the training configurations. For more details, please run ``./vits/train.py --help``.
+
+.. note::
+
+    The training can take a long time (usually a couple of days).
+
+Training logs, checkpoints and tensorboard logs are saved in ``vits/exp``.
+
+
+Inference
+---------
+
+The inference part uses checkpoints saved by the training part, so you have to run the
+training part first. It will save the ground-truth and generated wavs to the directory
+``vits/exp/infer/epoch-*/wav``, e.g., ``vits/exp/infer/epoch-1000/wav``.
+
+.. code-block:: bash
+
+  $ export CUDA_VISIBLE_DEVICES="0"
+  $ ./vits/infer.py \
+      --epoch 1000 \
+      --exp-dir vits/exp \
+      --tokens data/tokens.txt \
+      --max-duration 500
+
+.. note::
+
+    For more details, please run ``./vits/infer.py --help``.
+
+
+Export models
+-------------
+
+Currently we only support ONNX model exporting. It will generate two files in the given ``exp-dir``:
+``vits-epoch-*.onnx`` and ``vits-epoch-*.int8.onnx``.
+
+.. code-block:: bash
+
+  $ ./vits/export-onnx.py \
+      --epoch 1000 \
+      --exp-dir vits/exp \
+      --tokens data/tokens.txt
+
+You can test the exported ONNX model with:
+
+.. code-block:: bash
+
+  $ ./vits/test_onnx.py \
+      --model-filename vits/exp/vits-epoch-1000.onnx \
+      --tokens data/tokens.txt
+
+
+Download pretrained models
+--------------------------
+
+If you don't want to train from scratch, you can download the pretrained models
+by visiting the following link:
+
+  - `<https://huggingface.co/zrjin/icefall-tts-vctk-vits-2023-12-05>`_

egs/ljspeech/TTS/prepare.sh

@@ -5,8 +5,7 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
 
 set -eou pipefail
 
-nj=1
-stage=-1
+stage=0
 stop_stage=100
 
 dl_dir=$PWD/download
@@ -25,6 +24,17 @@ log() {
 
 log "dl_dir: $dl_dir"
 
+if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
+  log "Stage -1: build monotonic_align lib"
+  if [ ! -d vits/monotonic_align/build ]; then
+    cd vits/monotonic_align
+    python setup.py build_ext --inplace
+    cd ../../
+  else 
+    log "monotonic_align lib already built"
+  fi
+fi
+
 if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
   log "Stage 0: Download data"
 
@@ -113,5 +123,3 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
       --tokens data/tokens.txt
   fi
 fi
-
-

egs/ljspeech/TTS/vits/duration_predictor.py

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

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

@@ -180,7 +180,13 @@ def export_model_onnx(
         model_filename,
         verbose=False,
         opset_version=opset_version,
-        input_names=["tokens", "tokens_lens", "noise_scale", "noise_scale_dur", "alpha"],
+        input_names=[
+            "tokens",
+            "tokens_lens",
+            "noise_scale",
+            "noise_scale_dur",
+            "alpha",
+        ],
         output_names=["audio"],
         dynamic_axes={
             "tokens": {0: "N", 1: "T"},

egs/ljspeech/TTS/vits/flow.py

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

egs/ljspeech/TTS/vits/generator.py

@@ -16,9 +16,6 @@ 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
@@ -26,6 +23,8 @@ 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

egs/ljspeech/TTS/vits/infer.py

@@ -36,13 +36,12 @@ import k2
 import torch
 import torch.nn as nn
 import torchaudio
-
-from train import get_model, get_params
 from tokenizer import Tokenizer
+from train import get_model, get_params
+from tts_datamodule import LJSpeechTtsDataModule
 
 from icefall.checkpoint import load_checkpoint
 from icefall.utils import AttributeDict, setup_logger
-from tts_datamodule import LJSpeechTtsDataModule
 
 
 def get_parser():
@@ -144,14 +143,24 @@ def infer_dataset(
             audio_lens = batch["audio_lens"].tolist()
             cut_ids = [cut.id for cut in batch["cut"]]
 
-            audio_pred, _, durations = model.inference_batch(text=tokens, text_lengths=tokens_lens)
+            audio_pred, _, durations = model.inference_batch(
+                text=tokens, text_lengths=tokens_lens
+            )
             audio_pred = audio_pred.detach().cpu()
             # convert to samples
-            audio_lens_pred = (durations.sum(1) * params.frame_shift).to(dtype=torch.int64).tolist()
+            audio_lens_pred = (
+                (durations.sum(1) * params.frame_shift).to(dtype=torch.int64).tolist()
+            )
 
             futures.append(
                 executor.submit(
-                    _save_worker, batch_size, cut_ids, audio, audio_pred, audio_lens, audio_lens_pred
+                    _save_worker,
+                    batch_size,
+                    cut_ids,
+                    audio,
+                    audio_pred,
+                    audio_lens,
+                    audio_lens_pred,
                 )
             )
 
@@ -160,7 +169,9 @@ def infer_dataset(
             if batch_idx % log_interval == 0:
                 batch_str = f"{batch_idx}/{num_batches}"
 
-                logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+                logging.info(
+                    f"batch {batch_str}, cuts processed until now is {num_cuts}"
+                )
         # return results
         for f in futures:
             f.result()

egs/ljspeech/TTS/vits/loss.py

@@ -14,7 +14,6 @@ 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/ljspeech/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/ljspeech/TTS/vits/residual_coupling.py

@@ -12,7 +12,6 @@ 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/ljspeech/TTS/vits/test_onnx.py

@@ -28,10 +28,10 @@ Use the onnx model to generate a wav:
 
 import argparse
 import logging
+
 import onnxruntime as ort
 import torch
 import torchaudio
-
 from tokenizer import Tokenizer
 
 

egs/ljspeech/TTS/vits/text_encoder.py

@@ -169,9 +169,7 @@ class Transformer(nn.Module):
         x, pos_emb = self.encoder_pos(x)
         x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
 
-        x = self.encoder(
-            x, pos_emb, key_padding_mask=key_padding_mask
-        )  # (T, N, C)
+        x = self.encoder(x, pos_emb, key_padding_mask=key_padding_mask)  # (T, N, C)
 
         x = self.after_norm(x)
 
@@ -207,7 +205,9 @@ class TransformerEncoderLayer(nn.Module):
             nn.Linear(dim_feedforward, d_model),
         )
 
-        self.self_attn = RelPositionMultiheadAttention(d_model, num_heads, dropout=dropout)
+        self.self_attn = RelPositionMultiheadAttention(
+            d_model, num_heads, dropout=dropout
+        )
 
         self.conv_module = ConvolutionModule(d_model, cnn_module_kernel)
 
@@ -242,7 +242,9 @@ class TransformerEncoderLayer(nn.Module):
             key_padding_mask: the mask for the src keys per batch, of shape (batch_size, seq_len)
         """
         # macaron style feed-forward module
-        src = src + self.ff_scale * self.dropout(self.feed_forward_macaron(self.norm_ff_macaron(src)))
+        src = src + self.ff_scale * self.dropout(
+            self.feed_forward_macaron(self.norm_ff_macaron(src))
+        )
 
         # multi-head self-attention module
         src_attn = self.self_attn(
@@ -490,11 +492,17 @@ class RelPositionMultiheadAttention(nn.Module):
 
         q = q.contiguous().view(seq_len, batch_size, self.num_heads, self.head_dim)
         k = k.contiguous().view(seq_len, batch_size, self.num_heads, self.head_dim)
-        v = v.contiguous().view(seq_len, batch_size * self.num_heads, self.head_dim).transpose(0, 1)
+        v = (
+            v.contiguous()
+            .view(seq_len, batch_size * self.num_heads, self.head_dim)
+            .transpose(0, 1)
+        )
 
         q = q.transpose(0, 1)  # (batch_size, seq_len, num_head, head_dim)
 
-        p = self.linear_pos(pos_emb).view(pos_emb.size(0), -1, self.num_heads, self.head_dim)
+        p = self.linear_pos(pos_emb).view(
+            pos_emb.size(0), -1, self.num_heads, self.head_dim
+        )
         # (1, 2*seq_len, num_head, head_dim) -> (1, num_head, head_dim, 2*seq_len-1)
         p = p.permute(0, 2, 3, 1)
 
@@ -506,15 +514,23 @@ class RelPositionMultiheadAttention(nn.Module):
         # first compute matrix a and matrix c
         # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3
         k = k.permute(1, 2, 3, 0)  # (batch_size, num_head, head_dim, seq_len)
-        matrix_ac = torch.matmul(q_with_bias_u, k)  # (batch_size, num_head, seq_len, seq_len)
+        matrix_ac = torch.matmul(
+            q_with_bias_u, k
+        )  # (batch_size, num_head, seq_len, seq_len)
 
         # compute matrix b and matrix d
-        matrix_bd = torch.matmul(q_with_bias_v, p)  # (batch_size, num_head, seq_len, 2*seq_len-1)
-        matrix_bd = self.rel_shift(matrix_bd)  # (batch_size, num_head, seq_len, seq_len)
+        matrix_bd = torch.matmul(
+            q_with_bias_v, p
+        )  # (batch_size, num_head, seq_len, 2*seq_len-1)
+        matrix_bd = self.rel_shift(
+            matrix_bd
+        )  # (batch_size, num_head, seq_len, seq_len)
 
         # (batch_size, num_head, seq_len, seq_len)
         attn_output_weights = (matrix_ac + matrix_bd) * scaling
-        attn_output_weights = attn_output_weights.view(batch_size * self.num_heads, seq_len, seq_len)
+        attn_output_weights = attn_output_weights.view(
+            batch_size * self.num_heads, seq_len, seq_len
+        )
 
         if key_padding_mask is not None:
             assert key_padding_mask.shape == (batch_size, seq_len)
@@ -536,10 +552,16 @@ class RelPositionMultiheadAttention(nn.Module):
 
         # (batch_size * num_head, seq_len, head_dim)
         attn_output = torch.bmm(attn_output_weights, v)
-        assert attn_output.shape == (batch_size * self.num_heads, seq_len, self.head_dim)
+        assert attn_output.shape == (
+            batch_size * self.num_heads,
+            seq_len,
+            self.head_dim,
+        )
 
         attn_output = (
-            attn_output.transpose(0, 1).contiguous().view(seq_len, batch_size, self.embed_dim)
+            attn_output.transpose(0, 1)
+            .contiguous()
+            .view(seq_len, batch_size, self.embed_dim)
         )
         # (seq_len, batch_size, embed_dim)
         attn_output = self.out_proj(attn_output)

egs/ljspeech/TTS/vits/tokenizer.py

@@ -78,7 +78,9 @@ class Tokenizer(object):
 
         return token_ids_list
 
-    def tokens_to_token_ids(self, tokens_list: List[str], intersperse_blank: bool = True):
+    def tokens_to_token_ids(
+        self, tokens_list: List[str], intersperse_blank: bool = True
+    ):
         """
         Args:
           tokens_list:

egs/ljspeech/TTS/vits/train.py

@@ -18,21 +18,25 @@
 
 import argparse
 import logging
-import numpy as np
 from pathlib import Path
 from shutil import copyfile
 from typing import Any, Dict, Optional, Tuple, Union
 
 import k2
+import numpy as np
 import torch
 import torch.multiprocessing as mp
 import torch.nn as nn
 from lhotse.cut import Cut
 from lhotse.utils import fix_random_seed
-from torch.optim import Optimizer
+from tokenizer import Tokenizer
 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 utils import MetricsTracker, plot_feature, save_checkpoint
+from vits import VITS
 
 from icefall import diagnostics
 from icefall.checkpoint import load_checkpoint
@@ -41,11 +45,6 @@ from icefall.env import get_env_info
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import AttributeDict, setup_logger, str2bool
 
-from tokenizer import Tokenizer
-from tts_datamodule import LJSpeechTtsDataModule
-from utils import MetricsTracker, plot_feature, save_checkpoint
-from vits import VITS
-
 LRSchedulerType = torch.optim.lr_scheduler._LRScheduler
 
 
@@ -385,11 +384,12 @@ def train_one_epoch(
         params.batch_idx_train += 1
 
         batch_size = len(batch["tokens"])
-        audio, audio_lens, features, features_lens, tokens, tokens_lens = \
-            prepare_input(batch, tokenizer, device)
+        audio, audio_lens, features, features_lens, tokens, tokens_lens = prepare_input(
+            batch, tokenizer, device
+        )
 
         loss_info = MetricsTracker()
-        loss_info['samples'] = batch_size
+        loss_info["samples"] = batch_size
 
         try:
             with autocast(enabled=params.use_fp16):
@@ -446,7 +446,9 @@ def train_one_epoch(
             # behavior depending on the current grad scale.
             cur_grad_scale = scaler._scale.item()
 
-            if cur_grad_scale < 8.0 or (cur_grad_scale < 32.0 and params.batch_idx_train % 400 == 0):
+            if cur_grad_scale < 8.0 or (
+                cur_grad_scale < 32.0 and params.batch_idx_train % 400 == 0
+            ):
                 scaler.update(cur_grad_scale * 2.0)
             if cur_grad_scale < 0.01:
                 if not saved_bad_model:
@@ -482,9 +484,7 @@ def train_one_epoch(
                 loss_info.write_summary(
                     tb_writer, "train/current_", params.batch_idx_train
                 )
-                tot_loss.write_summary(
-                    tb_writer, "train/tot_", params.batch_idx_train
-                )
+                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
                 if params.use_fp16:
                     tb_writer.add_scalar(
                         "train/grad_scale", cur_grad_scale, params.batch_idx_train
@@ -492,19 +492,34 @@ def train_one_epoch(
                 if "returned_sample" in stats_g:
                     speech_hat_, speech_, mel_hat_, mel_ = stats_g["returned_sample"]
                     tb_writer.add_audio(
-                        "train/speech_hat_", speech_hat_, params.batch_idx_train, params.sampling_rate
+                        "train/speech_hat_",
+                        speech_hat_,
+                        params.batch_idx_train,
+                        params.sampling_rate,
                     )
                     tb_writer.add_audio(
-                        "train/speech_", speech_, params.batch_idx_train, params.sampling_rate
+                        "train/speech_",
+                        speech_,
+                        params.batch_idx_train,
+                        params.sampling_rate,
                     )
                     tb_writer.add_image(
-                        "train/mel_hat_", plot_feature(mel_hat_), params.batch_idx_train, dataformats='HWC'
+                        "train/mel_hat_",
+                        plot_feature(mel_hat_),
+                        params.batch_idx_train,
+                        dataformats="HWC",
                     )
                     tb_writer.add_image(
-                        "train/mel_", plot_feature(mel_), params.batch_idx_train, dataformats='HWC'
+                        "train/mel_",
+                        plot_feature(mel_),
+                        params.batch_idx_train,
+                        dataformats="HWC",
                     )
 
-        if params.batch_idx_train % params.valid_interval == 0 and not params.print_diagnostics:
+        if (
+            params.batch_idx_train % params.valid_interval == 0
+            and not params.print_diagnostics
+        ):
             logging.info("Computing validation loss")
             valid_info, (speech_hat, speech) = compute_validation_loss(
                 params=params,
@@ -523,10 +538,16 @@ def train_one_epoch(
                     tb_writer, "train/valid_", params.batch_idx_train
                 )
                 tb_writer.add_audio(
-                    "train/valdi_speech_hat", speech_hat, params.batch_idx_train, params.sampling_rate
+                    "train/valdi_speech_hat",
+                    speech_hat,
+                    params.batch_idx_train,
+                    params.sampling_rate,
                 )
                 tb_writer.add_audio(
-                    "train/valdi_speech", speech, params.batch_idx_train, params.sampling_rate
+                    "train/valdi_speech",
+                    speech,
+                    params.batch_idx_train,
+                    params.sampling_rate,
                 )
 
     loss_value = tot_loss["generator_loss"] / tot_loss["samples"]
@@ -555,11 +576,17 @@ def compute_validation_loss(
     with torch.no_grad():
         for batch_idx, batch in enumerate(valid_dl):
             batch_size = len(batch["tokens"])
-            audio, audio_lens, features, features_lens, tokens, tokens_lens = \
-                prepare_input(batch, tokenizer, device)
+            (
+                audio,
+                audio_lens,
+                features,
+                features_lens,
+                tokens,
+                tokens_lens,
+            ) = prepare_input(batch, tokenizer, device)
 
             loss_info = MetricsTracker()
-            loss_info['samples'] = batch_size
+            loss_info["samples"] = batch_size
 
             # forward discriminator
             loss_d, stats_d = model(
@@ -599,8 +626,13 @@ def compute_validation_loss(
                     text=tokens[0, : tokens_lens[0].item()]
                 )
                 audio_pred = audio_pred.data.cpu().numpy()
-                audio_len_pred = (duration.sum(0) * params.frame_shift).to(dtype=torch.int64).item()
-                assert audio_len_pred == len(audio_pred), (audio_len_pred, len(audio_pred))
+                audio_len_pred = (
+                    (duration.sum(0) * params.frame_shift).to(dtype=torch.int64).item()
+                )
+                assert audio_len_pred == len(audio_pred), (
+                    audio_len_pred,
+                    len(audio_pred),
+                )
                 audio_gt = audio[0, : audio_lens[0].item()].data.cpu().numpy()
                 returned_sample = (audio_pred, audio_gt)
 
@@ -632,8 +664,9 @@ def scan_pessimistic_batches_for_oom(
     batches, crit_values = find_pessimistic_batches(train_dl.sampler)
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
-        audio, audio_lens, features, features_lens, tokens, tokens_lens = \
-            prepare_input(batch, tokenizer, device)
+        audio, audio_lens, features, features_lens, tokens, tokens_lens = prepare_input(
+            batch, tokenizer, device
+        )
         try:
             # for discriminator
             with autocast(enabled=params.use_fp16):

egs/ljspeech/TTS/vits/tts_datamodule.py

@@ -29,10 +29,10 @@ from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
     CutConcatenate,
     CutMix,
     DynamicBucketingSampler,
-    SpeechSynthesisDataset,
     PrecomputedFeatures,
     SimpleCutSampler,
     SpecAugment,
+    SpeechSynthesisDataset,
 )
 from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
     AudioSamples,

egs/ljspeech/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.nn as nn
 import torch.distributed as dist
+import torch.nn as nn
 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
@@ -97,23 +97,23 @@ def plot_feature(spectrogram):
     global MATPLOTLIB_FLAG
     if not MATPLOTLIB_FLAG:
         import matplotlib
+
         matplotlib.use("Agg")
         MATPLOTLIB_FLAG = True
-        mpl_logger = logging.getLogger('matplotlib')
+        mpl_logger = logging.getLogger("matplotlib")
         mpl_logger.setLevel(logging.WARNING)
     import matplotlib.pylab as plt
     import numpy as np
 
     fig, ax = plt.subplots(figsize=(10, 2))
-    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                   interpolation='none')
+    im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
     plt.colorbar(im, ax=ax)
     plt.xlabel("Frames")
     plt.ylabel("Channels")
     plt.tight_layout()
 
     fig.canvas.draw()
-    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
     data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
     plt.close()
     return data

egs/ljspeech/TTS/vits/vits.py

@@ -9,8 +9,7 @@ from typing import Any, Dict, Optional, Tuple
 
 import torch
 import torch.nn as nn
-from torch.cuda.amp import autocast
-
+from generator import VITSGenerator
 from hifigan import (
     HiFiGANMultiPeriodDiscriminator,
     HiFiGANMultiScaleDiscriminator,
@@ -25,9 +24,8 @@ 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,
@@ -42,8 +40,7 @@ AVAILABLE_DISCRIMINATORS = {
 
 
 class VITS(nn.Module):
-    """Implement VITS, `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`
-    """
+    """Implement VITS, `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech`"""
 
     def __init__(
         self,

egs/ljspeech/TTS/vits/wavenet.py

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

egs/vctk/TTS/local/compute_spectrogram_vctk.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python3
+# Copyright    2021-2023  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                       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 computes fbank features of the VCTK dataset.
+It looks for manifests in the directory data/manifests.
+
+The generated fbank features are saved in data/spectrogram.
+"""
+
+import logging
+import os
+from pathlib import Path
+
+import torch
+from lhotse import (
+    CutSet,
+    LilcomChunkyWriter,
+    Spectrogram,
+    SpectrogramConfig,
+    load_manifest,
+)
+from lhotse.audio import RecordingSet
+from lhotse.supervision import SupervisionSet
+
+from icefall.utils import get_executor
+
+# Torch's multithreaded behavior needs to be disabled or
+# it wastes a lot of CPU and slow things down.
+# Do this outside of main() in case it needs to take effect
+# even when we are not invoking the main (e.g. when spawning subprocesses).
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+
+def compute_spectrogram_vctk():
+    src_dir = Path("data/manifests")
+    output_dir = Path("data/spectrogram")
+    num_jobs = min(32, os.cpu_count())
+
+    sampling_rate = 22050
+    frame_length = 1024 / sampling_rate  # (in second)
+    frame_shift = 256 / sampling_rate  # (in second)
+    use_fft_mag = True
+
+    prefix = "vctk"
+    suffix = "jsonl.gz"
+    partition = "all"
+
+    recordings = load_manifest(
+        src_dir / f"{prefix}_recordings_{partition}.jsonl.gz", RecordingSet
+    ).resample(sampling_rate=sampling_rate)
+    supervisions = load_manifest(
+        src_dir / f"{prefix}_supervisions_{partition}.jsonl.gz", SupervisionSet
+    )
+
+    config = SpectrogramConfig(
+        sampling_rate=sampling_rate,
+        frame_length=frame_length,
+        frame_shift=frame_shift,
+        use_fft_mag=use_fft_mag,
+    )
+    extractor = Spectrogram(config)
+
+    with get_executor() as ex:  # Initialize the executor only once.
+        cuts_filename = f"{prefix}_cuts_{partition}.{suffix}"
+        if (output_dir / cuts_filename).is_file():
+            logging.info(f"{partition} already exists - skipping.")
+            return
+        logging.info(f"Processing {partition}")
+        cut_set = CutSet.from_manifests(
+            recordings=recordings, supervisions=supervisions
+        )
+
+        cut_set = cut_set.compute_and_store_features(
+            extractor=extractor,
+            storage_path=f"{output_dir}/{prefix}_feats_{partition}",
+            # when an executor is specified, make more partitions
+            num_jobs=num_jobs if ex is None else 80,
+            executor=ex,
+            storage_type=LilcomChunkyWriter,
+        )
+        cut_set.to_file(output_dir / cuts_filename)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    compute_spectrogram_vctk()

egs/vctk/TTS/local/display_manifest_statistics.py

@@ -0,0 +1,83 @@
+#!/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 displays duration statistics of utterances in a manifest.
+You can use the displayed value to choose minimum/maximum duration
+to remove short and long utterances during the training.
+
+See the function `remove_short_and_long_utt()` in vits/train.py
+for usage.
+"""
+
+
+from lhotse import load_manifest_lazy
+
+
+def main():
+    path = "./data/spectrogram/vctk_cuts_all.jsonl.gz"
+    cuts = load_manifest_lazy(path)
+    cuts.describe()
+
+
+if __name__ == "__main__":
+    main()
+
+"""
+Cut statistics:
+╒═══════════════════════════╤══════════╕
+│ Cuts count:               │ 43873    │
+├───────────────────────────┼──────────┤
+│ Total duration (hh:mm:ss) │ 41:02:18 │
+├───────────────────────────┼──────────┤
+│ mean                      │ 3.4      │
+├───────────────────────────┼──────────┤
+│ std                       │ 1.2      │
+├───────────────────────────┼──────────┤
+│ min                       │ 1.2      │
+├───────────────────────────┼──────────┤
+│ 25%                       │ 2.6      │
+├───────────────────────────┼──────────┤
+│ 50%                       │ 3.1      │
+├───────────────────────────┼──────────┤
+│ 75%                       │ 3.8      │
+├───────────────────────────┼──────────┤
+│ 99%                       │ 8.0      │
+├───────────────────────────┼──────────┤
+│ 99.5%                     │ 9.1      │
+├───────────────────────────┼──────────┤
+│ 99.9%                     │ 12.1     │
+├───────────────────────────┼──────────┤
+│ max                       │ 16.6     │
+├───────────────────────────┼──────────┤
+│ Recordings available:     │ 43873    │
+├───────────────────────────┼──────────┤
+│ Features available:       │ 43873    │
+├───────────────────────────┼──────────┤
+│ Supervisions available:   │ 43873    │
+╘═══════════════════════════╧══════════╛
+SUPERVISION custom fields:
+Speech duration statistics:
+╒══════════════════════════════╤══════════╤══════════════════════╕
+│ Total speech duration        │ 41:02:18 │ 100.00% of recording │
+├──────────────────────────────┼──────────┼──────────────────────┤
+│ Total speaking time duration │ 41:02:18 │ 100.00% of recording │
+├──────────────────────────────┼──────────┼──────────────────────┤
+│ Total silence duration       │ 00:00:01 │ 0.00% of recording   │
+╘══════════════════════════════╧══════════╧══════════════════════╛
+"""

egs/vctk/TTS/local/prepare_token_file.py

@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+# Copyright         2023  Xiaomi Corp.        (authors: 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 file reads the texts in given manifest and generates the file that maps tokens to IDs.
+"""
+
+import argparse
+import logging
+from pathlib import Path
+from typing import Dict
+
+from lhotse import load_manifest
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--manifest-file",
+        type=Path,
+        default=Path("data/spectrogram/vctk_cuts_all.jsonl.gz"),
+        help="Path to the manifest file",
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=Path,
+        default=Path("data/tokens.txt"),
+        help="Path to the tokens",
+    )
+
+    return parser.parse_args()
+
+
+def write_mapping(filename: str, sym2id: Dict[str, int]) -> None:
+    """Write a symbol to ID mapping to a file.
+
+    Note:
+      No need to implement `read_mapping` as it can be done
+      through :func:`k2.SymbolTable.from_file`.
+
+    Args:
+      filename:
+        Filename to save the mapping.
+      sym2id:
+        A dict mapping symbols to IDs.
+    Returns:
+      Return None.
+    """
+    with open(filename, "w", encoding="utf-8") as f:
+        for sym, i in sym2id.items():
+            f.write(f"{sym} {i}\n")
+
+
+def get_token2id(manifest_file: Path) -> Dict[str, int]:
+    """Return a dict that maps token to IDs."""
+    extra_tokens = [
+        "<blk>",  # 0 for blank
+        "<sos/eos>",  # 1 for sos and eos symbols.
+        "<unk>",  # 2 for OOV
+    ]
+    all_tokens = 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)
+        for t in cut.tokens:
+            all_tokens.add(t)
+
+    all_tokens = extra_tokens + list(all_tokens)
+
+    token2id: Dict[str, int] = {token: i for i, token in enumerate(all_tokens)}
+    return token2id
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    args = get_args()
+    manifest_file = Path(args.manifest_file)
+    out_file = Path(args.tokens)
+
+    token2id = get_token2id(manifest_file)
+    write_mapping(out_file, token2id)

egs/vctk/TTS/local/prepare_tokens_vctk.py

@@ -0,0 +1,61 @@
+#!/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
+from tqdm.auto import tqdm
+
+
+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 tqdm(cut_set):
+        # Each cut only contains one supervision
+        assert len(cut.supervisions) == 1, len(cut.supervisions)
+        text = cut.supervisions[0].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/local/validate_manifest.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+# Copyright    2022-2023  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                       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 checks the following assumptions of the generated manifest:
+
+- Single supervision per cut
+
+We will add more checks later if needed.
+
+Usage example:
+
+    python3 ./local/validate_manifest.py \
+            ./data/spectrogram/ljspeech_cuts_all.jsonl.gz
+
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+from lhotse import CutSet, load_manifest_lazy
+from lhotse.dataset.speech_synthesis import validate_for_tts
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "manifest",
+        type=Path,
+        help="Path to the manifest file",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+
+    manifest = args.manifest
+    logging.info(f"Validating {manifest}")
+
+    assert manifest.is_file(), f"{manifest} does not exist"
+    cut_set = load_manifest_lazy(manifest)
+    assert isinstance(cut_set, CutSet)
+
+    validate_for_tts(cut_set)
+
+
+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/prepare.sh

@@ -0,0 +1,131 @@
+#!/usr/bin/env bash
+
+# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+set -eou pipefail
+
+stage=0
+stop_stage=100
+
+dl_dir=$PWD/download
+
+. shared/parse_options.sh || exit 1
+
+# All files generated by this script are saved in "data".
+# You can safely remove "data" and rerun this script to regenerate it.
+mkdir -p data
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+log "dl_dir: $dl_dir"
+
+if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
+  log "Stage -1: build monotonic_align lib"
+  if [ ! -d vits/monotonic_align/build ]; then
+    cd vits/monotonic_align
+    python setup.py build_ext --inplace
+    cd ../../
+  else 
+    log "monotonic_align lib already built"
+  fi
+fi
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "Stage 0: Download data"
+
+  # If you have pre-downloaded it to /path/to/VCTK,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/VCTK $dl_dir/VCTK
+  #
+  if [ ! -d $dl_dir/VCTK ]; then
+    lhotse download vctk $dl_dir
+  fi
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Prepare VCTK manifest"
+  # We assume that you have downloaded the VCTK corpus
+  # to $dl_dir/VCTK
+  mkdir -p data/manifests
+  if [ ! -e data/manifests/.vctk.done ]; then
+    lhotse prepare vctk --use-edinburgh-vctk-url true $dl_dir/VCTK data/manifests
+    touch data/manifests/.vctk.done
+  fi
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Compute spectrogram for VCTK"
+  mkdir -p data/spectrogram
+  if [ ! -e data/spectrogram/.vctk.done ]; then
+    ./local/compute_spectrogram_vctk.py
+    touch data/spectrogram/.vctk.done
+  fi
+
+  if [ ! -e data/spectrogram/.vctk-validated.done ]; then
+    log "Validating data/fbank for VCTK"
+    ./local/validate_manifest.py \
+      data/spectrogram/vctk_cuts_all.jsonl.gz
+    touch data/spectrogram/.vctk-validated.done
+  fi
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  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 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 4: Split the VCTK cuts into train, valid and test sets"
+  if [ ! -e data/spectrogram/.vctk_split.done ]; then
+    lhotse subset --last 600 \
+      data/spectrogram/vctk_cuts_all.jsonl.gz \
+      data/spectrogram/vctk_cuts_validtest.jsonl.gz
+    lhotse subset --first 100 \
+      data/spectrogram/vctk_cuts_validtest.jsonl.gz \
+      data/spectrogram/vctk_cuts_valid.jsonl.gz
+    lhotse subset --last 500 \
+      data/spectrogram/vctk_cuts_validtest.jsonl.gz \
+      data/spectrogram/vctk_cuts_test.jsonl.gz
+
+    rm data/spectrogram/vctk_cuts_validtest.jsonl.gz
+
+    n=$(( $(gunzip -c data/spectrogram/vctk_cuts_all.jsonl.gz | wc -l) - 600 ))
+    lhotse subset --first $n  \
+      data/spectrogram/vctk_cuts_all.jsonl.gz \
+      data/spectrogram/vctk_cuts_train.jsonl.gz
+      touch data/spectrogram/.vctk_split.done
+  fi
+fi
+
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+  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 \
+      --tokens data/tokens.txt
+  fi
+fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Generate speakers file"
+  if [ ! -e data/speakers.txt ]; then
+    gunzip -c data/manifests/vctk_supervisions_all.jsonl.gz \
+      | jq '.speaker' | sed 's/"//g' \
+      | sort | uniq > data/speakers.txt
+  fi
+fi

egs/vctk/TTS/shared

@@ -0,0 +1 @@
+../../../icefall/shared/

egs/vctk/TTS/vits/duration_predictor.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/duration_predictor.py

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

@@ -0,0 +1,284 @@
+#!/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(
+        "--speakers",
+        type=Path,
+        default=Path("data/speakers.txt"),
+        help="Path to speakers.txt file.",
+    )
+    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,
+        speaker: int = 20,
+        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.
+          speaker (int):
+            Speaker ID.
+          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,
+            sids=speaker,
+            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)
+    speaker = torch.tensor([1], dtype=torch.int64)
+
+    torch.onnx.export(
+        model,
+        (tokens, tokens_lens, noise_scale, noise_scale_dur, speaker, alpha),
+        model_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=[
+            "tokens",
+            "tokens_lens",
+            "noise_scale",
+            "noise_scale_dur",
+            "speaker",
+            "alpha",
+        ],
+        output_names=["audio"],
+        dynamic_axes={
+            "tokens": {0: "N", 1: "T"},
+            "tokens_lens": {0: "N"},
+            "audio": {0: "N", 1: "T"},
+            "speaker": {0: "N"},
+        },
+    )
+
+    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
+
+    with open(args.speakers) as f:
+        speaker_map = {line.strip(): i for i, line in enumerate(f)}
+    params.num_spks = len(speaker_map)
+
+    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

@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/flow.py

egs/vctk/TTS/vits/generator.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/generator.py

egs/vctk/TTS/vits/hifigan.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/hifigan.py

egs/vctk/TTS/vits/infer.py

@@ -0,0 +1,272 @@
+#!/usr/bin/env python3
+#
+# Copyright      2023 Xiaomi Corporation     (Author: 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 script performs model inference on test set.
+
+Usage:
+./vits/infer.py \
+    --epoch 1000 \
+    --exp-dir ./vits/exp \
+    --max-duration 500
+"""
+
+
+import argparse
+import logging
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+from typing import Dict, List
+
+import k2
+import torch
+import torch.nn as nn
+import torchaudio
+from tokenizer import Tokenizer
+from train import get_model, get_params
+from tts_datamodule import VctkTtsDataModule
+
+from icefall.checkpoint import load_checkpoint
+from icefall.utils import AttributeDict, setup_logger
+
+
+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 infer_dataset(
+    dl: torch.utils.data.DataLoader,
+    subset: str,
+    params: AttributeDict,
+    model: nn.Module,
+    tokenizer: Tokenizer,
+    speaker_map: Dict[str, int],
+) -> None:
+    """Decode dataset.
+    The ground-truth and generated audio pairs will be saved to `params.save_wav_dir`.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      tokenizer:
+        Used to convert text to phonemes.
+    """
+
+    #  Background worker save audios to disk.
+    def _save_worker(
+        subset: str,
+        batch_size: int,
+        cut_ids: List[str],
+        audio: torch.Tensor,
+        audio_pred: torch.Tensor,
+        audio_lens: List[int],
+        audio_lens_pred: List[int],
+    ):
+        for i in range(batch_size):
+            torchaudio.save(
+                str(params.save_wav_dir / subset / f"{cut_ids[i]}_gt.wav"),
+                audio[i : i + 1, : audio_lens[i]],
+                sample_rate=params.sampling_rate,
+            )
+            torchaudio.save(
+                str(params.save_wav_dir / subset / f"{cut_ids[i]}_pred.wav"),
+                audio_pred[i : i + 1, : audio_lens_pred[i]],
+                sample_rate=params.sampling_rate,
+            )
+
+    device = next(model.parameters()).device
+    num_cuts = 0
+    log_interval = 5
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    futures = []
+    with ThreadPoolExecutor(max_workers=1) as executor:
+        for batch_idx, batch in enumerate(dl):
+            batch_size = len(batch["tokens"])
+
+            tokens = batch["tokens"]
+            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]
+            tokens = tokens.to(device)
+            tokens_lens = tokens_lens.to(device)
+            # tensor of shape (B, T)
+            tokens = tokens.pad(mode="constant", padding_value=tokenizer.blank_id)
+            speakers = (
+                torch.Tensor([speaker_map[sid] for sid in batch["speakers"]])
+                .int()
+                .to(device)
+            )
+
+            audio = batch["audio"]
+            audio_lens = batch["audio_lens"].tolist()
+            cut_ids = [cut.id for cut in batch["cut"]]
+
+            audio_pred, _, durations = model.inference_batch(
+                text=tokens,
+                text_lengths=tokens_lens,
+                sids=speakers,
+            )
+            audio_pred = audio_pred.detach().cpu()
+            # convert to samples
+            audio_lens_pred = (
+                (durations.sum(1) * params.frame_shift).to(dtype=torch.int64).tolist()
+            )
+
+            futures.append(
+                executor.submit(
+                    _save_worker,
+                    subset,
+                    batch_size,
+                    cut_ids,
+                    audio,
+                    audio_pred,
+                    audio_lens,
+                    audio_lens_pred,
+                )
+            )
+
+            num_cuts += batch_size
+
+            if batch_idx % log_interval == 0:
+                batch_str = f"{batch_idx}/{num_batches}"
+
+                logging.info(
+                    f"batch {batch_str}, cuts processed until now is {num_cuts}"
+                )
+        # return results
+        for f in futures:
+            f.result()
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    VctkTtsDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    params.suffix = f"epoch-{params.epoch}"
+
+    params.res_dir = params.exp_dir / "infer" / params.suffix
+    params.save_wav_dir = params.res_dir / "wav"
+    params.save_wav_dir.mkdir(parents=True, exist_ok=True)
+
+    setup_logger(f"{params.res_dir}/log-infer-{params.suffix}")
+    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.blank_id
+    params.oov_id = tokenizer.oov_id
+    params.vocab_size = tokenizer.vocab_size
+
+    # we need cut ids to display recognition results.
+    args.return_cuts = True
+    vctk = VctkTtsDataModule(args)
+    speaker_map = vctk.speakers()
+    params.num_spks = len(speaker_map)
+
+    logging.info(f"Device: {device}")
+    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()
+
+    num_param_g = sum([p.numel() for p in model.generator.parameters()])
+    logging.info(f"Number of parameters in generator: {num_param_g}")
+    num_param_d = sum([p.numel() for p in model.discriminator.parameters()])
+    logging.info(f"Number of parameters in discriminator: {num_param_d}")
+    logging.info(f"Total number of parameters: {num_param_g + num_param_d}")
+
+    test_cuts = vctk.test_cuts()
+    test_dl = vctk.test_dataloaders(test_cuts)
+
+    valid_cuts = vctk.valid_cuts()
+    valid_dl = vctk.valid_dataloaders(valid_cuts)
+
+    infer_sets = {"test": test_dl, "valid": valid_dl}
+
+    for subset, dl in infer_sets.items():
+        save_wav_dir = params.res_dir / "wav" / subset
+        save_wav_dir.mkdir(parents=True, exist_ok=True)
+
+        logging.info(f"Processing {subset} set, saving to {save_wav_dir}")
+
+        infer_dataset(
+            dl=dl,
+            subset=subset,
+            params=params,
+            model=model,
+            tokenizer=tokenizer,
+            speaker_map=speaker_map,
+        )
+
+    logging.info(f"Wav files are saved to {params.save_wav_dir}")
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()

egs/vctk/TTS/vits/loss.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/loss.py

egs/vctk/TTS/vits/monotonic_align

@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/monotonic_align

egs/vctk/TTS/vits/posterior_encoder.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/posterior_encoder.py

egs/vctk/TTS/vits/residual_coupling.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/residual_coupling.py

egs/vctk/TTS/vits/test_onnx.py

@@ -0,0 +1,138 @@
+#!/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
+from pathlib import Path
+
+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(
+        "--speakers",
+        type=Path,
+        default=Path("data/speakers.txt"),
+        help="Path to speakers.txt file.",
+    )
+    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, speaker: 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: speaker.numpy(),
+                self.model.get_inputs()[5].name: alpha.numpy(),
+            },
+        )[0]
+        return torch.from_numpy(out)
+
+
+def main():
+    args = get_parser().parse_args()
+
+    tokenizer = Tokenizer(args.tokens)
+
+    with open(args.speakers) as f:
+        speaker_map = {line.strip(): i for i, line in enumerate(f)}
+    args.num_spks = len(speaker_map)
+
+    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)
+    speaker = torch.tensor([1], dtype=torch.int64)  # (1, )
+    audio = model(tokens, tokens_lens, speaker)  # (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

@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/text_encoder.py

egs/vctk/TTS/vits/tokenizer.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/tokenizer.py

egs/vctk/TTS/vits/transform.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/transform.py

egs/vctk/TTS/vits/tts_datamodule.py

@@ -0,0 +1,338 @@
+# Copyright      2021  Piotr Żelasko
+# Copyright      2022-2023  Xiaomi Corporation     (Authors: Mingshuang Luo,
+#                                                            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.
+
+
+import argparse
+import logging
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+import torch
+from lhotse import CutSet, Spectrogram, SpectrogramConfig, load_manifest_lazy
+from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
+    CutConcatenate,
+    CutMix,
+    DynamicBucketingSampler,
+    PrecomputedFeatures,
+    SimpleCutSampler,
+    SpecAugment,
+    SpeechSynthesisDataset,
+)
+from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
+    AudioSamples,
+    OnTheFlyFeatures,
+)
+from lhotse.utils import fix_random_seed
+from torch.utils.data import DataLoader
+
+from icefall.utils import str2bool
+
+
+class _SeedWorkers:
+    def __init__(self, seed: int):
+        self.seed = seed
+
+    def __call__(self, worker_id: int):
+        fix_random_seed(self.seed + worker_id)
+
+
+class VctkTtsDataModule:
+    """
+    DataModule for tts experiments.
+    It assumes there is always one train and valid dataloader,
+    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
+    and test-other).
+
+    It contains all the common data pipeline modules used in ASR
+    experiments, e.g.:
+    - dynamic batch size,
+    - bucketing samplers,
+    - cut concatenation,
+    - on-the-fly feature extraction
+
+    This class should be derived for specific corpora used in ASR tasks.
+    """
+
+    def __init__(self, args: argparse.Namespace):
+        self.args = args
+
+    @classmethod
+    def add_arguments(cls, parser: argparse.ArgumentParser):
+        group = parser.add_argument_group(
+            title="TTS data related options",
+            description="These options are used for the preparation of "
+            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
+            "effective batch sizes, sampling strategies, applied data "
+            "augmentations, etc.",
+        )
+
+        group.add_argument(
+            "--manifest-dir",
+            type=Path,
+            default=Path("data/spectrogram"),
+            help="Path to directory with train/valid/test cuts.",
+        )
+        group.add_argument(
+            "--speakers",
+            type=Path,
+            default=Path("data/speakers.txt"),
+            help="Path to speakers.txt file.",
+        )
+        group.add_argument(
+            "--max-duration",
+            type=int,
+            default=200.0,
+            help="Maximum pooled recordings duration (seconds) in a "
+            "single batch. You can reduce it if it causes CUDA OOM.",
+        )
+        group.add_argument(
+            "--bucketing-sampler",
+            type=str2bool,
+            default=True,
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
+        )
+        group.add_argument(
+            "--num-buckets",
+            type=int,
+            default=30,
+            help="The number of buckets for the DynamicBucketingSampler"
+            "(you might want to increase it for larger datasets).",
+        )
+
+        group.add_argument(
+            "--on-the-fly-feats",
+            type=str2bool,
+            default=False,
+            help="When enabled, use on-the-fly cut mixing and feature "
+            "extraction. Will drop existing precomputed feature manifests "
+            "if available.",
+        )
+        group.add_argument(
+            "--shuffle",
+            type=str2bool,
+            default=True,
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
+        )
+        group.add_argument(
+            "--drop-last",
+            type=str2bool,
+            default=True,
+            help="Whether to drop last batch. Used by sampler.",
+        )
+        group.add_argument(
+            "--return-cuts",
+            type=str2bool,
+            default=False,
+            help="When enabled, each batch will have the "
+            "field: batch['cut'] with the cuts that "
+            "were used to construct it.",
+        )
+        group.add_argument(
+            "--num-workers",
+            type=int,
+            default=8,
+            help="The number of training dataloader workers that "
+            "collect the batches.",
+        )
+
+        group.add_argument(
+            "--input-strategy",
+            type=str,
+            default="PrecomputedFeatures",
+            help="AudioSamples or PrecomputedFeatures",
+        )
+
+    def train_dataloaders(
+        self,
+        cuts_train: CutSet,
+        sampler_state_dict: Optional[Dict[str, Any]] = None,
+    ) -> DataLoader:
+        """
+        Args:
+          cuts_train:
+            CutSet for training.
+          sampler_state_dict:
+            The state dict for the training sampler.
+        """
+        logging.info("About to create train dataset")
+        train = SpeechSynthesisDataset(
+            return_text=False,
+            return_tokens=True,
+            return_spk_ids=True,
+            feature_input_strategy=eval(self.args.input_strategy)(),
+            return_cuts=self.args.return_cuts,
+        )
+
+        if self.args.on_the_fly_feats:
+            sampling_rate = 22050
+            config = SpectrogramConfig(
+                sampling_rate=sampling_rate,
+                frame_length=1024 / sampling_rate,  # (in second),
+                frame_shift=256 / sampling_rate,  # (in second)
+                use_fft_mag=True,
+            )
+            train = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=OnTheFlyFeatures(Spectrogram(config)),
+                return_cuts=self.args.return_cuts,
+            )
+
+        if self.args.bucketing_sampler:
+            logging.info("Using DynamicBucketingSampler.")
+            train_sampler = DynamicBucketingSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+                num_buckets=self.args.num_buckets,
+                drop_last=self.args.drop_last,
+            )
+        else:
+            logging.info("Using SimpleCutSampler.")
+            train_sampler = SimpleCutSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+            )
+        logging.info("About to create train dataloader")
+
+        if sampler_state_dict is not None:
+            logging.info("Loading sampler state dict")
+            train_sampler.load_state_dict(sampler_state_dict)
+
+        # 'seed' is derived from the current random state, which will have
+        # previously been set in the main process.
+        seed = torch.randint(0, 100000, ()).item()
+        worker_init_fn = _SeedWorkers(seed)
+
+        train_dl = DataLoader(
+            train,
+            sampler=train_sampler,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            persistent_workers=False,
+            worker_init_fn=worker_init_fn,
+        )
+
+        return train_dl
+
+    def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
+        logging.info("About to create dev dataset")
+        if self.args.on_the_fly_feats:
+            sampling_rate = 22050
+            config = SpectrogramConfig(
+                sampling_rate=sampling_rate,
+                frame_length=1024 / sampling_rate,  # (in second),
+                frame_shift=256 / sampling_rate,  # (in second)
+                use_fft_mag=True,
+            )
+            validate = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=OnTheFlyFeatures(Spectrogram(config)),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            validate = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=eval(self.args.input_strategy)(),
+                return_cuts=self.args.return_cuts,
+            )
+        valid_sampler = DynamicBucketingSampler(
+            cuts_valid,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.info("About to create valid dataloader")
+        valid_dl = DataLoader(
+            validate,
+            sampler=valid_sampler,
+            batch_size=None,
+            num_workers=2,
+            persistent_workers=False,
+        )
+
+        return valid_dl
+
+    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
+        logging.info("About to create test dataset")
+        if self.args.on_the_fly_feats:
+            sampling_rate = 22050
+            config = SpectrogramConfig(
+                sampling_rate=sampling_rate,
+                frame_length=1024 / sampling_rate,  # (in second),
+                frame_shift=256 / sampling_rate,  # (in second)
+                use_fft_mag=True,
+            )
+            test = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=OnTheFlyFeatures(Spectrogram(config)),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            test = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=eval(self.args.input_strategy)(),
+                return_cuts=self.args.return_cuts,
+            )
+        test_sampler = DynamicBucketingSampler(
+            cuts,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.info("About to create test dataloader")
+        test_dl = DataLoader(
+            test,
+            batch_size=None,
+            sampler=test_sampler,
+            num_workers=self.args.num_workers,
+        )
+        return test_dl
+
+    @lru_cache()
+    def train_cuts(self) -> CutSet:
+        logging.info("About to get train cuts")
+        return load_manifest_lazy(self.args.manifest_dir / "vctk_cuts_train.jsonl.gz")
+
+    @lru_cache()
+    def valid_cuts(self) -> CutSet:
+        logging.info("About to get validation cuts")
+        return load_manifest_lazy(self.args.manifest_dir / "vctk_cuts_valid.jsonl.gz")
+
+    @lru_cache()
+    def test_cuts(self) -> CutSet:
+        logging.info("About to get test cuts")
+        return load_manifest_lazy(self.args.manifest_dir / "vctk_cuts_test.jsonl.gz")
+
+    @lru_cache()
+    def speakers(self) -> Dict[str, int]:
+        logging.info("About to get speakers")
+        with open(self.args.speakers) as f:
+            speakers = {line.strip(): i for i, line in enumerate(f)}
+        return speakers

egs/vctk/TTS/vits/utils.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/utils.py

egs/vctk/TTS/vits/vits.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/vits.py

egs/vctk/TTS/vits/wavenet.py

@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/wavenet.py

requirements-tts.txt

@@ -0,0 +1,6 @@
+# for TTS recipes
+matplotlib==3.8.2
+cython==3.0.6
+numba==0.58.1
+g2p_en==2.1.0
+espnet_tts_frontend==0.0.3

requirements.txt

@@ -8,3 +8,5 @@ tensorboard
 typeguard
 dill
 black==22.3.0
+onnx==1.15.0
+onnxruntime==1.16.3