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minor updates
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@ -4,6 +4,10 @@ VITS
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This tutorial shows you how to train an VITS model
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This tutorial shows you how to train an VITS model
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with the `LJSpeech <https://keithito.com/LJ-Speech-Dataset/>`_ dataset.
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with the `LJSpeech <https://keithito.com/LJ-Speech-Dataset/>`_ dataset.
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.. note::
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TTS related recipes require packages in ``requirements-tts.txt``.
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.. note::
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.. note::
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The VITS paper: `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech <https://arxiv.org/pdf/2106.06103.pdf>`_
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The VITS paper: `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech <https://arxiv.org/pdf/2106.06103.pdf>`_
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@ -27,6 +31,12 @@ To run stage 1 to stage 5, use
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Build Monotonic Alignment Search
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Build Monotonic Alignment Search
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--------------------------------
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--------------------------------
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.. code-block:: bash
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$ ./prepare.sh --stage -1 --stop_stage -1
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or
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.. code-block:: bash
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.. code-block:: bash
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$ cd vits/monotonic_align
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$ cd vits/monotonic_align
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@ -4,6 +4,10 @@ VITS
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This tutorial shows you how to train an VITS model
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This tutorial shows you how to train an VITS model
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with the `VCTK <https://datashare.ed.ac.uk/handle/10283/3443>`_ dataset.
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with the `VCTK <https://datashare.ed.ac.uk/handle/10283/3443>`_ dataset.
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.. note::
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TTS related recipes require packages in ``requirements-tts.txt``.
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.. note::
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.. note::
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The VITS paper: `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech <https://arxiv.org/pdf/2106.06103.pdf>`_
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The VITS paper: `Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech <https://arxiv.org/pdf/2106.06103.pdf>`_
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@ -31,7 +35,7 @@ To build the monotonic alignment search, use the following commands:
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.. code-block:: bash
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.. code-block:: bash
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$ ./prepare.sh --stage 7 --stop_stage 7
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$ ./prepare.sh --stage -1 --stop_stage -1
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or
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or
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@ -5,8 +5,7 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
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set -eou pipefail
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set -eou pipefail
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nj=1
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stage=0
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stage=-1
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stop_stage=100
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stop_stage=100
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dl_dir=$PWD/download
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dl_dir=$PWD/download
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@ -25,6 +24,17 @@ log() {
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log "dl_dir: $dl_dir"
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log "dl_dir: $dl_dir"
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if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
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log "Stage -1: build monotonic_align lib"
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if [ ! -d vits/monotonic_align/build ]; then
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cd vits/monotonic_align
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python setup.py build_ext --inplace
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cd ../../
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else
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log "monotonic_align lib already built"
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fi
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fi
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Download data"
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log "Stage 0: Download data"
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@ -113,5 +123,3 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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--tokens data/tokens.txt
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--tokens data/tokens.txt
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fi
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fi
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fi
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fi
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@ -5,8 +5,7 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
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set -eou pipefail
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set -eou pipefail
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nj=1
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stage=0
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stage=-1
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stop_stage=100
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stop_stage=100
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dl_dir=$PWD/download
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dl_dir=$PWD/download
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@ -25,6 +24,17 @@ log() {
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log "dl_dir: $dl_dir"
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log "dl_dir: $dl_dir"
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if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
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log "Stage -1: build monotonic_align lib"
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if [ ! -d vits/monotonic_align/build ]; then
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cd vits/monotonic_align
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python setup.py build_ext --inplace
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cd ../../
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else
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log "monotonic_align lib already built"
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fi
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fi
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Download data"
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log "Stage 0: Download data"
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@ -119,14 +129,3 @@ if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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| sort | uniq > data/speakers.txt
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| sort | uniq > data/speakers.txt
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fi
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fi
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fi
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fi
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if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 7: build monotonic_align lib"
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if [ ! -d vits/monotonic_align/build ]; then
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cd vits/monotonic_align
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python setup.py build_ext --inplace
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cd ../../
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else
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log "monotonic_align lib already built"
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fi
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fi
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