Add a TTS recipe VITS on LJSpeech dataset

.flake8

@@ -15,7 +15,7 @@ per-file-ignores =
     egs/librispeech/ASR/zipformer_mmi/*.py: E501, E203
     egs/librispeech/ASR/zipformer/*.py: E501, E203
     egs/librispeech/ASR/RESULTS.md: E999,
-
+    egs/ljspeech/TTS/vits/*.py: E501, E203
     # invalid escape sequence (cause by tex formular), W605
     icefall/utils.py: E501, W605
 

docs/source/recipes/TTS/index.rst

@@ -0,0 +1,7 @@
+TTS
+======
+
+.. toctree::
+   :maxdepth: 2
+
+   ljspeech/vits

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

@@ -0,0 +1,113 @@
+VITS
+===============
+
+This tutorial shows you how to train an VITS model
+with the `LJSpeech <https://keithito.com/LJ-Speech-Dataset/>`_ dataset.
+
+.. 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/ljspeech/TTS
+  $ ./prepare.sh
+
+To run stage 1 to stage 5, use
+
+.. code-block:: bash
+
+  $ ./prepare.sh --stage 1 --stop_stage 5
+
+
+Build Monotonic Alignment Search
+--------------------------------
+
+.. 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 500
+
+.. 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/Zengwei/icefall-tts-ljspeech-vits-2023-11-29>`_

docs/source/recipes/index.rst

@@ -2,7 +2,7 @@ Recipes
 =======
 
 This page contains various recipes in ``icefall``.
-Currently, only speech recognition recipes are provided.
+Currently, we provide recipes for speech recognition, language model, and speech synthesis.
 
 We may add recipes for other tasks as well in the future.
 
@@ -16,3 +16,4 @@ We may add recipes for other tasks as well in the future.
    Non-streaming-ASR/index
    Streaming-ASR/index
    RNN-LM/index
+   TTS/index

egs/ljspeech/TTS/local/compute_spectrogram_ljspeech.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python3
+# Copyright    2021-2023  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 file computes fbank features of the LJSpeech dataset.
+It looks for manifests in the directory data/manifests.
+
+The generated spectrogram 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_ljspeech():
+    src_dir = Path("data/manifests")
+    output_dir = Path("data/spectrogram")
+    num_jobs = min(4, 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 = "ljspeech"
+    suffix = "jsonl.gz"
+    partition = "all"
+
+    recordings = load_manifest(
+        src_dir / f"{prefix}_recordings_{partition}.{suffix}", RecordingSet
+    )
+    supervisions = load_manifest(
+        src_dir / f"{prefix}_supervisions_{partition}.{suffix}", 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"{cuts_filename} 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_ljspeech()

egs/ljspeech/TTS/local/display_manifest_statistics.py

@@ -0,0 +1,73 @@
+#!/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 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/ljspeech_cuts_all.jsonl.gz"
+    cuts = load_manifest_lazy(path)
+    cuts.describe()
+
+
+if __name__ == "__main__":
+    main()
+
+"""
+Cut statistics:
+    ╒═══════════════════════════╤══════════╕
+    │ Cuts count:               │ 13100    │
+    ├───────────────────────────┼──────────┤
+    │ Total duration (hh:mm:ss) │ 23:55:18 │
+    ├───────────────────────────┼──────────┤
+    │ mean                      │ 6.6      │
+    ├───────────────────────────┼──────────┤
+    │ std                       │ 2.2      │
+    ├───────────────────────────┼──────────┤
+    │ min                       │ 1.1      │
+    ├───────────────────────────┼──────────┤
+    │ 25%                       │ 5.0      │
+    ├───────────────────────────┼──────────┤
+    │ 50%                       │ 6.8      │
+    ├───────────────────────────┼──────────┤
+    │ 75%                       │ 8.4      │
+    ├───────────────────────────┼──────────┤
+    │ 99%                       │ 10.0     │
+    ├───────────────────────────┼──────────┤
+    │ 99.5%                     │ 10.1     │
+    ├───────────────────────────┼──────────┤
+    │ 99.9%                     │ 10.1     │
+    ├───────────────────────────┼──────────┤
+    │ max                       │ 10.1     │
+    ├───────────────────────────┼──────────┤
+    │ Recordings available:     │ 13100    │
+    ├───────────────────────────┼──────────┤
+    │ Features available:       │ 13100    │
+    ├───────────────────────────┼──────────┤
+    │ Supervisions available:   │ 13100    │
+    ╘═══════════════════════════╧══════════╛
+"""