Add joint tacotron2_hifigan config

training/STEPS.md

@@ -5,7 +5,7 @@ Link: https://espnet.github.io/espnet/installation.html
 sudo apt-get install cmake sox libsndfile1-dev ffmpeg
 git clone --branch v.202301 https://github.com/espnet/espnet
 cd ./espnet/tools
-./setup_anaconda.sh anaconda espnet 3.8
+./setup_anaconda.sh anaconda espnet 3.10
 . ./activate_python.sh
 make
 pip install --upgrade torch torchaudio # or setup same versions
@@ -19,15 +19,17 @@ ESPNET is a dynamic framework. For the latest guide, please refer to https://git
 
 This page provides general launching steps on how training was performed for reference, and this doesn't cover data preparation.
 
-NOTE: before running the script below, copy [./train_vits.yaml](./train_vits.yaml) to your `<espnet_root>/egs2/ljspeech/tts1/conf/tuning/train_vits.yaml`
+NOTE: before running the script below, copy [./train_vits.yaml](./train_vits.yaml) or [./finetune_joint_tacotron2_hifigan.yaml](./finetune_joint_tacotron2_hifigan.yaml) to your `<espnet_root>/egs2/ljspeech/tts1/conf/tuning/` folder
 
 
 ```sh
 cd ../egs2/ljspeech/tts1
 pip install torchvision # to save figures
-pip install speechbrain
+pip install speechbrain # for x-vectors
+# option 1: train VITS
 ./run.sh \
-    --stage 2 \
+    --stage 6 \
+    --min_wav_duration 0.38 \
     --use_xvector true \
     --xvector_tool speechbrain \
     --fs 22050 \
@@ -41,4 +43,21 @@ pip install speechbrain
     --feats_normalize none \
     --train_config ./conf/tuning/train_vits.yaml \
     --inference_config ./conf/tuning/decode_vits.yaml
-```
+# option 2: train tacotron2 and hifigan jointly
+./run.sh \
+    --stage 6 \
+    --min_wav_duration 0.38 \
+    --use_xvector true \
+    --xvector_tool speechbrain \
+    --fs 22050 \
+    --n_fft 1024 \
+    --n_shift 256 \
+    --win_length null \
+    --dumpdir dump/22k \
+    --expdir exp/22k \
+    --train_config ./conf/tuning/finetune_joint_tacotron2_hifigan.yaml \
+    --tts_task gan_tts
+
+```
+
+

training/esp_test.ipynb

@@ -1,114 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#@title Choose English model { run: \"auto\" }\n",
-    "lang = 'English'\n",
-    "tag = 'training/espnet/egs2/ljspeech/tts1' #@param [\"kan-bayashi/ljspeech_tacotron2\", \"kan-bayashi/ljspeech_fastspeech\", \"kan-bayashi/ljspeech_fastspeech2\", \"kan-bayashi/ljspeech_conformer_fastspeech2\", \"kan-bayashi/ljspeech_joint_finetune_conformer_fastspeech2_hifigan\", \"kan-bayashi/ljspeech_joint_train_conformer_fastspeech2_hifigan\", \"kan-bayashi/ljspeech_vits\"] {type:\"string\"}\n",
-    "vocoder_tag = \"none\" #@param [\"none\", \"parallel_wavegan/ljspeech_parallel_wavegan.v1\", \"parallel_wavegan/ljspeech_full_band_melgan.v2\", \"parallel_wavegan/ljspeech_multi_band_melgan.v2\", \"parallel_wavegan/ljspeech_hifigan.v1\", \"parallel_wavegan/ljspeech_style_melgan.v1\"] {type:\"string\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/feats_stats.npz'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[7], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mespnet2\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mbin\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mtts_inference\u001b[39;00m \u001b[39mimport\u001b[39;00m Text2Speech\n\u001b[1;32m      2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mespnet2\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutils\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mtypes\u001b[39;00m \u001b[39mimport\u001b[39;00m str_or_none\n\u001b[0;32m----> 4\u001b[0m text2speech \u001b[39m=\u001b[39m Text2Speech(\n\u001b[1;32m      5\u001b[0m     train_config\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m/home/robinhad/Projects/ukrainian-tts/training/espnet/egs2/ljspeech/tts1/exp/tts_train_raw_phn_tacotron_g2p_en_no_space/config.yaml\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m      6\u001b[0m     model_file\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m/home/robinhad/Projects/ukrainian-tts/training/espnet/egs2/ljspeech/tts1/exp/tts_train_raw_phn_tacotron_g2p_en_no_space/checkpoint.pth\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m      7\u001b[0m     device\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mcuda\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m      8\u001b[0m     \u001b[39m# Only for Tacotron 2 & Transformer\u001b[39;49;00m\n\u001b[1;32m      9\u001b[0m     threshold\u001b[39m=\u001b[39;49m\u001b[39m0.5\u001b[39;49m,\n\u001b[1;32m     10\u001b[0m     \u001b[39m# Only for Tacotron 2\u001b[39;49;00m\n\u001b[1;32m     11\u001b[0m     minlenratio\u001b[39m=\u001b[39;49m\u001b[39m0.0\u001b[39;49m,\n\u001b[1;32m     12\u001b[0m     maxlenratio\u001b[39m=\u001b[39;49m\u001b[39m10.0\u001b[39;49m,\n\u001b[1;32m     13\u001b[0m     use_att_constraint\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m     14\u001b[0m     backward_window\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m,\n\u001b[1;32m     15\u001b[0m     forward_window\u001b[39m=\u001b[39;49m\u001b[39m3\u001b[39;49m,\n\u001b[1;32m     16\u001b[0m     \u001b[39m# Only for FastSpeech & FastSpeech2 & VITS\u001b[39;49;00m\n\u001b[1;32m     17\u001b[0m     speed_control_alpha\u001b[39m=\u001b[39;49m\u001b[39m4\u001b[39;49m,\n\u001b[1;32m     18\u001b[0m     \u001b[39m# Only for VITS\u001b[39;49;00m\n\u001b[1;32m     19\u001b[0m     noise_scale\u001b[39m=\u001b[39;49m\u001b[39m0.333\u001b[39;49m,\n\u001b[1;32m     20\u001b[0m     noise_scale_dur\u001b[39m=\u001b[39;49m\u001b[39m0.333\u001b[39;49m,\n\u001b[1;32m     21\u001b[0m )\n",
-      "File \u001b[0;32m~/Projects/ukrainian-tts/training/espnet/espnet2/bin/tts_inference.py:92\u001b[0m, in \u001b[0;36mText2Speech.__init__\u001b[0;34m(self, train_config, model_file, threshold, minlenratio, maxlenratio, use_teacher_forcing, use_att_constraint, backward_window, forward_window, speed_control_alpha, noise_scale, noise_scale_dur, vocoder_config, vocoder_file, dtype, device, seed, always_fix_seed, prefer_normalized_feats)\u001b[0m\n\u001b[1;32m     89\u001b[0m \u001b[39massert\u001b[39;00m check_argument_types()\n\u001b[1;32m     91\u001b[0m \u001b[39m# setup model\u001b[39;00m\n\u001b[0;32m---> 92\u001b[0m model, train_args \u001b[39m=\u001b[39m TTSTask\u001b[39m.\u001b[39;49mbuild_model_from_file(\n\u001b[1;32m     93\u001b[0m     train_config, model_file, device\n\u001b[1;32m     94\u001b[0m )\n\u001b[1;32m     95\u001b[0m model\u001b[39m.\u001b[39mto(dtype\u001b[39m=\u001b[39m\u001b[39mgetattr\u001b[39m(torch, dtype))\u001b[39m.\u001b[39meval()\n\u001b[1;32m     96\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdevice \u001b[39m=\u001b[39m device\n",
-      "File \u001b[0;32m~/Projects/ukrainian-tts/training/espnet/espnet2/tasks/abs_task.py:1822\u001b[0m, in \u001b[0;36mAbsTask.build_model_from_file\u001b[0;34m(cls, config_file, model_file, device)\u001b[0m\n\u001b[1;32m   1820\u001b[0m     args \u001b[39m=\u001b[39m yaml\u001b[39m.\u001b[39msafe_load(f)\n\u001b[1;32m   1821\u001b[0m args \u001b[39m=\u001b[39m argparse\u001b[39m.\u001b[39mNamespace(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39margs)\n\u001b[0;32m-> 1822\u001b[0m model \u001b[39m=\u001b[39m \u001b[39mcls\u001b[39;49m\u001b[39m.\u001b[39;49mbuild_model(args)\n\u001b[1;32m   1823\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39misinstance\u001b[39m(model, AbsESPnetModel):\n\u001b[1;32m   1824\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\n\u001b[1;32m   1825\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mmodel must inherit \u001b[39m\u001b[39m{\u001b[39;00mAbsESPnetModel\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m, but got \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mtype\u001b[39m(model)\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m   1826\u001b[0m     )\n",
-      "File \u001b[0;32m~/Projects/ukrainian-tts/training/espnet/espnet2/tasks/tts.py:309\u001b[0m, in \u001b[0;36mTTSTask.build_model\u001b[0;34m(cls, args)\u001b[0m\n\u001b[1;32m    307\u001b[0m \u001b[39mif\u001b[39;00m args\u001b[39m.\u001b[39mnormalize \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    308\u001b[0m     normalize_class \u001b[39m=\u001b[39m normalize_choices\u001b[39m.\u001b[39mget_class(args\u001b[39m.\u001b[39mnormalize)\n\u001b[0;32m--> 309\u001b[0m     normalize \u001b[39m=\u001b[39m normalize_class(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49margs\u001b[39m.\u001b[39;49mnormalize_conf)\n\u001b[1;32m    310\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    311\u001b[0m     normalize \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/Projects/ukrainian-tts/training/espnet/espnet2/layers/global_mvn.py:40\u001b[0m, in \u001b[0;36mGlobalMVN.__init__\u001b[0;34m(self, stats_file, norm_means, norm_vars, eps)\u001b[0m\n\u001b[1;32m     37\u001b[0m stats_file \u001b[39m=\u001b[39m Path(stats_file)\n\u001b[1;32m     39\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstats_file \u001b[39m=\u001b[39m stats_file\n\u001b[0;32m---> 40\u001b[0m stats \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mload(stats_file)\n\u001b[1;32m     41\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(stats, np\u001b[39m.\u001b[39mndarray):\n\u001b[1;32m     42\u001b[0m     \u001b[39m# Kaldi like stats\u001b[39;00m\n\u001b[1;32m     43\u001b[0m     count \u001b[39m=\u001b[39m stats[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mflatten()[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]\n",
-      "File \u001b[0;32m~/.miniconda3/envs/espnet/lib/python3.8/site-packages/numpy/lib/npyio.py:390\u001b[0m, in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    388\u001b[0m     own_fid \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m    389\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 390\u001b[0m     fid \u001b[39m=\u001b[39m stack\u001b[39m.\u001b[39menter_context(\u001b[39mopen\u001b[39;49m(os_fspath(file), \u001b[39m\"\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m\"\u001b[39;49m))\n\u001b[1;32m    391\u001b[0m     own_fid \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    393\u001b[0m \u001b[39m# Code to distinguish from NumPy binary files and pickles.\u001b[39;00m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/feats_stats.npz'"
-     ]
-    }
-   ],
-   "source": [
-    "from espnet2.bin.tts_inference import Text2Speech\n",
-    "from espnet2.utils.types import str_or_none\n",
-    "\n",
-    "text2speech = Text2Speech(\n",
-    "    train_config=\"exp/tts_train_raw_phn_tacotron_g2p_en_no_space/config.yaml\",\n",
-    "    model_file=\"exp/tts_train_raw_phn_tacotron_g2p_en_no_space/checkpoint.pth\",\n",
-    "    device=\"cuda\",\n",
-    "    # Only for Tacotron 2 & Transformer\n",
-    "    threshold=0.5,\n",
-    "    # Only for Tacotron 2\n",
-    "    minlenratio=0.0,\n",
-    "    maxlenratio=10.0,\n",
-    "    use_att_constraint=False,\n",
-    "    backward_window=1,\n",
-    "    forward_window=3,\n",
-    "    # Only for FastSpeech & FastSpeech2 & VITS\n",
-    "    speed_control_alpha=4,\n",
-    "    # Only for VITS\n",
-    "    noise_scale=0.333,\n",
-    "    noise_scale_dur=0.333,\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "import torch\n",
-    "\n",
-    "# decide the input sentence by yourself\n",
-    "print(f\"Input your favorite sentence in {lang}.\")\n",
-    "x = input()\n",
-    "\n",
-    "# synthesis\n",
-    "with torch.no_grad():\n",
-    "    start = time.time()\n",
-    "    wav = text2speech(x)[\"wav\"]\n",
-    "rtf = (time.time() - start) / (len(wav) / text2speech.fs)\n",
-    "print(f\"RTF = {rtf:5f}\")\n",
-    "\n",
-    "# let us listen to generated samples\n",
-    "from IPython.display import display, Audio\n",
-    "display(Audio(wav.view(-1).cpu().numpy(), rate=text2speech.fs))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.8.15 ('espnet')",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.15"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "baacc56cbf39183fce53815df8d7ef29797de9f36fbce345069f80337ea8dac3"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

training/finetune_joint_tacotron2_hifigan.yaml

@@ -0,0 +1,255 @@
+# This EXPERIMENTAL configuration is for ESPnet2 to finetune
+# Conformer FastSpeech2 + HiFiGAN vocoder jointly. To run
+# this config, you need to specify "--tts_task gan_tts"
+# option for tts.sh at least and use 22050 hz audio as the
+# training data (mainly tested on LJspeech).
+# This configuration tested on 4 GPUs with 12GB GPU memory.
+# It takes around less than 1 week to finish the training but
+# 100k iters model should generate reasonable results.
+
+# YOU NEED TO MODIFY THE "*_params" AND "init_param" SECTIONS
+# IF YOU WANT TO USE YOUR OWN PRETRAINED MODLES.
+
+##########################################################
+#                  TTS MODEL SETTING                     #
+##########################################################
+tts: joint_text2wav
+tts_conf:
+    # copied from pretrained model's config.yaml
+    text2mel_type: tacotron2
+    text2mel_params:
+        embed_dim: 512               # char or phn embedding dimension
+        elayers: 1                   # number of blstm layers in encoder
+        eunits: 512                  # number of blstm units
+        econv_layers: 3              # number of convolutional layers in encoder
+        econv_chans: 512             # number of channels in convolutional layer
+        econv_filts: 5               # filter size of convolutional layer
+        atype: location              # attention function type
+        adim: 512                    # attention dimension
+        aconv_chans: 32              # number of channels in convolutional layer of attention
+        aconv_filts: 15              # filter size of convolutional layer of attention
+        cumulate_att_w: true         # whether to cumulate attention weight
+        dlayers: 2                   # number of lstm layers in decoder
+        dunits: 1024                 # number of lstm units in decoder
+        prenet_layers: 2             # number of layers in prenet
+        prenet_units: 256            # number of units in prenet
+        postnet_layers: 5            # number of layers in postnet
+        postnet_chans: 512           # number of channels in postnet
+        postnet_filts: 5             # filter size of postnet layer
+        output_activation: null      # activation function for the final output
+        use_batch_norm: true         # whether to use batch normalization in encoder
+        use_concate: true            # whether to concatenate encoder embedding with decoder outputs
+        use_residual: false          # whether to use residual connection in encoder
+        spk_embed_dim: 192           # speaker embedding dimension
+        spk_embed_integration_type: add # how to integrate speaker embedding
+        dropout_rate: 0.5            # dropout rate
+        zoneout_rate: 0.1            # zoneout rate
+        reduction_factor: 1          # reduction factor
+        use_masking: true            # whether to apply masking for padded part in loss calculation
+        bce_pos_weight: 10.0         # weight of positive sample in binary cross entropy calculation
+        use_guided_attn_loss: true   # whether to use guided attention loss
+        guided_attn_loss_sigma: 0.4  # sigma of guided attention loss
+        guided_attn_loss_lambda: 1.0 # strength of guided attention loss
+
+    # copied from pretrained vocoder's config.yaml
+    vocoder_type: hifigan_generator
+    vocoder_params:
+        bias: true
+        channels: 512
+        in_channels: 80
+        kernel_size: 7
+        nonlinear_activation: LeakyReLU
+        nonlinear_activation_params:
+            negative_slope: 0.1
+        out_channels: 1
+        resblock_dilations:
+        - - 1
+          - 3
+          - 5
+        - - 1
+          - 3
+          - 5
+        - - 1
+          - 3
+          - 5
+        resblock_kernel_sizes:
+        - 3
+        - 7
+        - 11
+        upsample_kernel_sizes:
+        - 16
+        - 16
+        - 4
+        - 4
+        upsample_scales:
+        - 8
+        - 8
+        - 2
+        - 2
+        use_additional_convs: true
+        use_weight_norm: true
+
+    # copied from pretrained vocoder's config.yaml
+    discriminator_type: hifigan_multi_scale_multi_period_discriminator
+    discriminator_params:
+        follow_official_norm: true
+        period_discriminator_params:
+            bias: true
+            channels: 32
+            downsample_scales:
+            - 3
+            - 3
+            - 3
+            - 3
+            - 1
+            in_channels: 1
+            kernel_sizes:
+            - 5
+            - 3
+            max_downsample_channels: 1024
+            nonlinear_activation: LeakyReLU
+            nonlinear_activation_params:
+                negative_slope: 0.1
+            out_channels: 1
+            use_spectral_norm: false
+            use_weight_norm: true
+        periods:
+        - 2
+        - 3
+        - 5
+        - 7
+        - 11
+        scale_discriminator_params:
+            bias: true
+            channels: 128
+            downsample_scales:
+            - 4
+            - 4
+            - 4
+            - 4
+            - 1
+            in_channels: 1
+            kernel_sizes:
+            - 15
+            - 41
+            - 5
+            - 3
+            max_downsample_channels: 1024
+            max_groups: 16
+            nonlinear_activation: LeakyReLU
+            nonlinear_activation_params:
+                negative_slope: 0.1
+            out_channels: 1
+        scale_downsample_pooling: AvgPool1d
+        scale_downsample_pooling_params:
+            kernel_size: 4
+            padding: 2
+            stride: 2
+        scales: 3
+
+    # loss function related
+    generator_adv_loss_params:
+        average_by_discriminators: false # whether to average loss value by #discriminators
+        loss_type: mse                   # loss type, "mse" or "hinge"
+    discriminator_adv_loss_params:
+        average_by_discriminators: false # whether to average loss value by #discriminators
+        loss_type: mse                   # loss type, "mse" or "hinge"
+    use_feat_match_loss: true            # whether to use feat match loss
+    feat_match_loss_params:
+        average_by_discriminators: false # whether to average loss value by #discriminators
+        average_by_layers: false         # whether to average loss value by #layers of each discriminator
+        include_final_outputs: true      # whether to include final outputs for loss calculation
+    use_mel_loss: true     # whether to use mel-spectrogram loss
+    mel_loss_params:
+        fs: 22050          # must be the same as the training data
+        n_fft: 1024        # fft points
+        hop_length: 256    # hop size
+        win_length: null   # window length
+        window: hann       # window type
+        n_mels: 80         # number of Mel basis
+        fmin: 0            # minimum frequency for Mel basis
+        fmax: null         # maximum frequency for Mel basis
+        log_base: null     # null represent natural log
+    lambda_text2mel: 1.0   # loss scaling coefficient for text2mel loss
+    lambda_adv: 1.0        # loss scaling coefficient for adversarial loss
+    lambda_mel: 45.0       # loss scaling coefficient for Mel loss
+    lambda_feat_match: 2.0 # loss scaling coefficient for feat match loss
+
+    # others
+    sampling_rate: 22050          # needed in the inference for saving wav
+    segment_size: 32              # segment size for random windowed discriminator
+    cache_generator_outputs: true # whether to cache generator outputs in the training
+
+# extra module for additional inputs
+#pitch_extract: dio           # pitch extractor type
+#pitch_extract_conf:
+#    reduction_factor: 1
+#pitch_normalize: global_mvn  # normalizer for the pitch feature
+#energy_extract: energy       # energy extractor type
+#energy_extract_conf:
+#    reduction_factor: 1
+#energy_normalize: global_mvn # normalizer for the energy feature
+
+# initialization (might need to modify for your own pretrained model)
+init_param:
+- exp/22k/tts_train_tacotron2_raw_char/train.loss.ave_5best.pth:tts:tts.generator.text2mel
+- exp/22k/ljspeech_hifigan.v1/generator.pth::tts.generator.vocoder
+- exp/22k/ljspeech_hifigan.v1/discriminator.pth::tts.discriminator
+
+##########################################################
+#            OPTIMIZER & SCHEDULER SETTING               #
+##########################################################
+# optimizer setting for generator
+optim: adam
+optim_conf:
+    lr: 1.25e-5
+    betas: [0.5, 0.9]
+    weight_decay: 0.0
+scheduler: exponentiallr
+scheduler_conf:
+    gamma: 0.999875
+# optimizer setting for discriminator
+optim2: adam
+optim2_conf:
+    lr: 1.25e-5
+    betas: [0.5, 0.9]
+    weight_decay: 0.0
+scheduler2: exponentiallr
+scheduler2_conf:
+    gamma: 0.999875
+generator_first: true # whether to start updating generator first
+
+##########################################################
+#                OTHER TRAINING SETTING                  #
+##########################################################
+#num_iters_per_epoch: 1000 # number of iterations per epoch
+max_epoch: 500            # number of epochs
+accum_grad: 1             # gradient accumulation
+batch_bins: 1600000       # batch bins (feats_type=raw)
+batch_type: numel         # how to make batch
+grad_clip: -1             # gradient clipping norm
+grad_noise: false         # whether to use gradient noise injection
+sort_in_batch: descending # how to sort data in making batch
+sort_batch: descending    # how to sort created batches
+num_workers: 4            # number of workers of data loader
+use_amp: false            # whether to use pytorch amp
+log_interval: 50          # log interval in iterations
+keep_nbest_models: 5      # number of models to keep
+num_att_plot: 3           # number of attention figures to be saved in every check
+seed: 777                 # random seed number
+patience: null            # patience for early stopping
+unused_parameters: true   # needed for multi gpu case
+best_model_criterion:     # criterion to save the best models
+-   - valid
+    - text2mel_loss
+    - min
+-   - train
+    - text2mel_loss
+    - min
+-   - train
+    - total_count
+    - max
+cudnn_deterministic: false # setting to false accelerates the training speed but makes it non-deterministic
+                           # in the case of GAN-TTS training, we strongly recommend setting to false
+cudnn_benchmark: false     # setting to true might acdelerate the training speed but sometimes decrease it
+                           # therefore, we set to false as a default (recommend trying both cases)

tts_example.ipynb

@@ -11,58 +11,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "downloading uk/mykyta/vits-tts\n",
-      "Found ./model.pth. Skipping download...\n",
-      "Found ./config.yaml. Skipping download...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/robinhad/Projects/ukrainian-tts/.venv/lib/python3.9/site-packages/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace`\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "RTF = 0.213155\n",
-      "Accented text: прив+іт, +як +у теб+е спр+ави?\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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\" type=\"audio/x-wav\" />\n",
-       "                    Your browser does not support the audio element.\n",
-       "                </audio>\n",
-       "              "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.Audio object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from ukrainian_tts.tts import TTS, Voices, Stress\n",
     "import IPython.display as ipd\n",
     "\n",
-    "tts = TTS(device=\"cpu\") # can try gpu, mps\n",
+    "tts = TTS(device=\"cpu\", cache_dir=\"model\") # can try gpu, mps\n",
     "with open(\"test.wav\", mode=\"wb\") as file:\n",
     "    _, output_text = tts.tts(\"Привіт, як у тебе справи?\", Voices.Dmytro.value, Stress.Dictionary.value, file)\n",
     "print(\"Accented text:\", output_text)\n",
@@ -72,24 +28,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<Voices.Olena: 4>,\n",
-       " <Voices.Mykyta: 3>,\n",
-       " <Voices.Lada: 2>,\n",
-       " <Voices.Dmytro: 1>,\n",
-       " <Voices.Olga: 5>]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "[voice for voice in Voices]"
    ]
@@ -111,7 +52,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.10.12"
   },
   "orig_nbformat": 4,
   "vscode": {