From 4cff8e43e80f9e707c0237b87d4272d54d67fbbf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CJionghao=E2=80=9D?= Date: Mon, 27 May 2024 21:29:29 -0400 Subject: [PATCH 1/6] espnetez gan_svs finetune --- .../config/kisingv2/finetune_with_lora.yaml | 17 + ESPnetEZ/svs_finetune_kising_v2.ipynb | 5405 +++++++++++++++++ 2 files changed, 5422 insertions(+) create mode 100644 ESPnetEZ/config/kisingv2/finetune_with_lora.yaml create mode 100644 ESPnetEZ/svs_finetune_kising_v2.ipynb diff --git a/ESPnetEZ/config/kisingv2/finetune_with_lora.yaml b/ESPnetEZ/config/kisingv2/finetune_with_lora.yaml new file mode 100644 index 00000000..4d8811c3 --- /dev/null +++ b/ESPnetEZ/config/kisingv2/finetune_with_lora.yaml @@ -0,0 +1,17 @@ +use_adapter: true +adapter: lora +save_strategy: adapter +adapter_conf: + rank: 4 + alpha: 4 + dropout_rate: 0.1 + target_modules: + - self_attn.linear_q + - self_attn.linear_k + - self_attn.linear_v + - self_attn.linear_out + - emb_phone + - emb_pitch + - emb_slur + - global_emb +freeze_param: ["svs.generator"] \ No newline at end of file diff --git a/ESPnetEZ/svs_finetune_kising_v2.ipynb b/ESPnetEZ/svs_finetune_kising_v2.ipynb new file mode 100644 index 00000000..f5f87049 --- /dev/null +++ b/ESPnetEZ/svs_finetune_kising_v2.ipynb @@ -0,0 +1,5405 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "rVaKdn4KzLWA" + }, + "source": [ + "# (Optional) Mounting your Google Drive" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uy0XYUVbcaWs" + }, + "outputs": [], + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "E_tUN5UrcnAE" + }, + "outputs": [], + "source": [ + "# %cd path_to_your_project\n", + "%cd 'drive/MyDrive/espnetez'\n", + "!pwd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0xmK-IOF0OHS" + }, + "source": [ + "# References\n", + "- [ESPnet repository](https://github.com/espnet/espnet)\n", + "- [ESPnet documentation](https://espnet.github.io/espnet/)\n", + "- [ESPnet-EZ repo](https://github.com/espnet/espnet/tree/master/espnetez)\n", + "- [libri100 ESPnetEZ training demo](https://github.com/Masao-Someki/espnet/blob/c7884444ca204049b045f1ba6f2297a34e904374/egsez/asr/libri100.ipynb)\n", + "- [OWSM ESPnetEZ finetuning demo](https://colab.research.google.com/drive/1Q-J0WUlvAIJbAJ-U_uKQqrvFtHUeWxus)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zc_5YHRXb7lB" + }, + "source": [ + "# Overview\n", + "- Task: Singing Voice Synthesis\n", + "- Dataset: *KiSing-v2*\n", + "- Model: *VISinger 2* model trained on *ACE-Opencpop* - [espnet/acesinger_opencpop_visinger2_44khz](https://huggingface.co/espnet/acesinger_opencpop_visinger2_44khz)\n", + "\n", + "This demo includes data preparation, finetuning, and inference." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q7H4TMNw0kDz" + }, + "source": [ + "# Prepare Environment\n", + "## Install ESPnet and related packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1uAyUBZ2dWJu", + "outputId": "4715d65f-9af8-43a0-94a2-abcc5fb80e1f" + }, + "outputs": [], + "source": [ + "# clone ESPnet's repo\n", + "!git clone https://github.com/espnet/espnet.git" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AUKTeK5Wb7lE", + "outputId": "7131c8f9-ed86-4bff-e2d2-69ae67a90187" + }, + "outputs": [], + "source": [ + "!cd espnet && pip install .\n", + "!pip install espnet_model_zoo tensorboard\n", + "!pip install loralib\n", + "!pip install datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import espnetez as ez" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define constants" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "TASK = \"gan_svs\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "05rJsG030eS8" + }, + "source": [ + "# Data Preparation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fJgZ8xJKohw5" + }, + "source": [ + "## Load dataset\n", + "KiSing-v2 on Hugging Face: [espnet/kising-v2-segments](https://huggingface.co/datasets/espnet/kising-v2-segments)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 560, + "referenced_widgets": [ + "acf6a19456e94aac9329cc0688a219de", + "95c6f187a23548b3a0eb4aa58a741435", + "bf5bd805a7014f6690002d611a02ee4c", + "37993f080cfc48fc990aa286511cedf1", + "7dd4450d7df74b6e84059cc395501182", + 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"6ce89e0d9433418d834fbf64a597e275", + "52b76e5ae11b4557b94269a24771fa8f", + "9a525de8fccf4b0bab21cdda3e6b3910", + "f64e22f459fd4b3d93b05c751624bec6", + "9e1e3a59af5049fb8a4a24c2c157cbbf", + "3df4255e87cb459d9c7feb6302247f6b", + "0d7356d7e1dd4a9fbd7dbf2c5b023032", + "dd46c071a51d4dcf97f7174bae2b8b63" + ] + }, + "id": "yRZUkK-4og8r", + "outputId": "e9ba09f3-22e1-453d-943f-954ff5b7efa0" + }, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "dataset = load_dataset(\"espnet/kising-v2-segments\", cache_dir=\"data\")\n", + "train_dataset = dataset['train']\n", + "valid_dataset = dataset['validation']\n", + "test_dataset = dataset['test']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xqKDni4zKLPy" + }, + "source": [ + "The first two instances of the train set:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "y-QMh5dAKIOm", + "outputId": "c766d4cd-d7ab-4a34-8411-6558c9c6367c" + }, + "outputs": [], + "source": [ + "it = iter(train_dataset)\n", + "next(it), next(it)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mZIuMy0bVLp9" + }, + "source": [ + "## Resample audio in accordance with the pretrained model\n", + "Sampling rate is 44.1k in [espnet/acesinger_opencpop_visinger2_44khz](https://huggingface.co/espnet/acesinger_opencpop_visinger2_44khz)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 180 + }, + "id": "a-xBk_4AVR4P", + "outputId": "fbda16ee-53ca-444d-9437-17e8eecf7713" + }, + "outputs": [], + "source": [ + "from datasets import Audio\n", + "train_dataset = train_dataset.cast_column(\"audio\", Audio(sampling_rate=44100))\n", + "valid_dataset = valid_dataset.cast_column(\"audio\", Audio(sampling_rate=44100))\n", + "test_dataset = test_dataset.cast_column(\"audio\", Audio(sampling_rate=44100))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CiH2uDJstjgz" + }, + "source": [ + "## Define data info for constructing a ESPnetEZ dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "singer2sid = {\n", + " \"barber\": 3,\n", + " \"blanca\": 30,\n", + " \"changge\": 5,\n", + " \"chuci\": 19,\n", + " \"chuming\": 4,\n", + " \"crimson\": 1,\n", + " \"david\": 28,\n", + " \"dvaid\": 31,\n", + " \"ghost\": 27,\n", + " \"growl\": 25,\n", + " \"hiragi-yuki\": 22,\n", + " \"huolian\": 13,\n", + " \"kuro\": 2,\n", + " \"lien\": 29,\n", + " \"liyuan\": 9,\n", + " \"luanming\": 21,\n", + " \"luotianyi\": 36,\n", + " \"namine\": 8,\n", + " \"orange\": 12,\n", + " \"original\": 32,\n", + " \"qifu\": 16,\n", + " \"qili\": 15,\n", + " \"qixuan\": 7,\n", + " \"quehe\": 6,\n", + " \"ranhuhu\": 11,\n", + " \"steel\": 26,\n", + " \"tangerine\": 23,\n", + " \"tarara\": 20,\n", + " \"tuyuan\": 24,\n", + " \"wenli\": 10,\n", + " \"xiaomo\": 17,\n", + " \"xiaoye\": 14,\n", + " \"yanhe\": 33,\n", + " \"yuehao\": 34,\n", + " \"yuezhengling\": 35,\n", + " \"yunhao\": 18\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5D6umH4wb7lF" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "data_info = {\n", + " \"singing\": lambda d: d['audio']['array'].astype(np.float32),\n", + " \"score\": lambda d: (d['tempo'], list(zip(*[d[key] for key in ('note_start_times', 'note_end_times', 'note_lyrics', 'note_midi', 'note_phns')]))),\n", + " \"text\": lambda d: d['transcription'],\n", + " \"label\": lambda d: (np.array(list(zip(*[d[key] for key in ('phn_start_time', 'phn_end_time')]))), d['phn']),\n", + " \"sids\": lambda d: np.array([singer2sid[d['singer']]]),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RqUXRreFzY_y" + }, + "outputs": [], + "source": [ + "inp = next(it)\n", + "inp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tNdCOz1bKlLS" + }, + "outputs": [], + "source": [ + "[(k, func(inp))for k, func in data_info.items()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load as ESPnetEZ dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset = ez.dataset.ESPnetEZDataset(train_dataset, data_info=data_info)\n", + "valid_dataset = ez.dataset.ESPnetEZDataset(valid_dataset, data_info=data_info)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OMmUTSVVb7lG" + }, + "source": [ + "# Finetuning" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hgRlRX2G9AgT" + }, + "source": [ + "## Set Up Configurations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LMFcKDhs7jLJ" + }, + "source": [ + "Download the [pretrained VISinger2 model on ACE-Opencpop](https://huggingface.co/espnet/acesinger_opencpop_visinger2_44khz) using espnet_model_zoo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4DlwQDkeJpVa" + }, + "outputs": [], + "source": [ + "PRETRAIN_MODEL = \"espnet/acesinger_opencpop_visinger2_44khz\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4z5szxVZJfr0" + }, + "outputs": [], + "source": [ + "from espnet_model_zoo.downloader import ModelDownloader\n", + "d = ModelDownloader()\n", + "pretrain_downloaded = d.download_and_unpack(PRETRAIN_MODEL)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m8PSENxm98ZF" + }, + "source": [ + "Load pretrained configuration" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UfGw3xYh98Id" + }, + "outputs": [], + "source": [ + "# load pretrained config\n", + "pretrain_config = ez.config.from_yaml(\n", + " TASK,\n", + " pretrain_downloaded['train_config'],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "W9j2-JFmJU4v" + }, + "source": [ + "Add the pretrained model path to the config file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lwpbXF5gMoa8" + }, + "outputs": [], + "source": [ + "pretrain_config['model_file'] = pretrain_downloaded['model_file']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kh_T8x-X8iPX" + }, + "source": [ + "Update our configuration for LoRA finetuning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_name='kisingv2'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SkxsJplmb7lH" + }, + "outputs": [], + "source": [ + "# update with lora finetuning config\n", + "finetune_config = ez.config.update_finetune_config(\n", + "\tTASK,\n", + "\tpretrain_config,\n", + "\tf\"./config/{dataset_name}/finetune_with_lora.yaml\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Update number of singers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "finetune_config['svs_conf']['generator_params']['spks'] = 36 + 1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1s-2p9muWn5N" + }, + "source": [ + "Clear local files in the original config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bkxkoKFeWs5V" + }, + "outputs": [], + "source": [ + "finetune_config['train_data_path_and_name_and_type'] = []\n", + "finetune_config['valid_data_path_and_name_and_type'] = []\n", + "finetune_config['train_shape_file'] = []\n", + "finetune_config['valid_shape_file'] = []\n", + "finetune_config['output_dir'] = None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use SVS preprocessor in ESPnet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "finetune_config[\"use_preprocessor\"] = True" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uA96KdcKLFuW" + }, + "source": [ + "Customize our fine-tuning config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hAtRx7JZLE_w" + }, + "outputs": [], + "source": [ + "finetune_config[\"batch_size\"] = 4\n", + "finetune_config[\"num_workers\"] = 1\n", + "finetune_config[\"max_epoch\"] = 10\n", + "finetune_config[\"num_iters_per_epoch\"] = None" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EPjWSrB78s0Z" + }, + "source": [ + "## Define a Trainer\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DMTHIKcb-McL" + }, + "outputs": [], + "source": [ + "EXP_DIR = f\"exp/finetune_{dataset_name}_{TASK}\"\n", + "STATS_DIR = f\"exp/stats_{dataset_name}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "AB1OqYh489Pu" + }, + "outputs": [], + "source": [ + "# Define the Trainer class\n", + "trainer = ez.Trainer(\n", + " task=TASK,\n", + " train_config=finetune_config,\n", + " train_dataset=train_dataset,\n", + " valid_dataset=valid_dataset,\n", + " data_info=data_info,\n", + " output_dir=EXP_DIR,\n", + " stats_dir=STATS_DIR,\n", + " ngpu=1,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Collect stats\n", + "To prepare the stats file before training, you can execute the `collect_stats` method. This step is required before the training process and ensuring accurate statistics for the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7q6jLEPOFp9O" + }, + "outputs": [], + "source": [ + "# set normalization to None when collecting stats, as we don't have the mean/var yet\n", + "trainer.train_config.normalize = None\n", + "trainer.train_config.pitch_normalize = None\n", + "trainer.train_config.energy_normalize = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Collect stats\n", + "trainer.collect_stats()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_KLr12H6PNXQ" + }, + "outputs": [], + "source": [ + "# set normalization back to the original config\n", + "trainer.train_config.normalize = finetune_config['normalize']\n", + "trainer.train_config.pitch_normalize = finetune_config['pitch_normalize']\n", + "trainer.train_config.normalize_conf['stats_file'] = f'{STATS_DIR}/train/feats_stats.npz'\n", + "trainer.train_config.pitch_normalize_conf['stats_file'] = f'{STATS_DIR}/train/pitch_stats.npz'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jroayQlwb7lI" + }, + "source": [ + "## Start training" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "d40GeNWsb7lI" + }, + "outputs": [], + "source": [ + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dUeu-y28b7lJ" + }, + "source": [ + "# Inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ULAzv_32-YRL" + }, + "outputs": [], + "source": [ + "from espnet2.bin.svs_inference import SingingGenerate" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1TvmC3jGAJPz" + }, + "source": [ + "## Set up model for inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pz3oSlKUAdS6" + }, + "outputs": [], + "source": [ + "m = SingingGenerate(\n", + " f\"{EXP_DIR}/config.yaml\",\n", + " f\"{EXP_DIR}/train.total_count.ave_10best.pth\",\n", + ")\n", + "\n", + "m.model.eval()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TWIVS2sPAeHA" + }, + "source": [ + "## Prepare data for inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VMkcwLxOAln-" + }, + "outputs": [], + "source": [ + "test_dataset = ez.dataset.ESPnetEZDataset(test_dataset, data_info=data_info)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WTbOx1BpAoJu" + }, + "source": [ + "## Run inference with test data\n", + "Here, we'll test the first instance in the test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wVTtK42Mb7lJ" + }, + "outputs": [], + "source": [ + "(key, batch) = next(iter(test_dataset)) # get the first instance\n", + "\n", + "batch.pop('singing')\n", + 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dropout_rate: 0.1 + target_modules: + - self_attn.linear_q + - self_attn.linear_k + - self_attn.linear_v + - self_attn.linear_out + - emb + +freeze_param: ["tts.generator"] \ No newline at end of file diff --git a/ESPnetEZ/svs_finetune_kising_v2.ipynb b/ESPnetEZ/svs_finetune_kising_v2.ipynb index f5f87049..73bcb6a6 100644 --- a/ESPnetEZ/svs_finetune_kising_v2.ipynb +++ b/ESPnetEZ/svs_finetune_kising_v2.ipynb @@ -1,39 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "rVaKdn4KzLWA" - }, - "source": [ - "# (Optional) Mounting your Google Drive" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "uy0XYUVbcaWs" - }, - "outputs": [], - "source": [ - "from google.colab import drive\n", - "drive.mount('/content/drive')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "E_tUN5UrcnAE" - }, - "outputs": [], - "source": [ - "# %cd path_to_your_project\n", - "%cd 'drive/MyDrive/espnetez'\n", - "!pwd" - ] - }, { "cell_type": "markdown", "metadata": { diff --git a/ESPnetEZ/tts_finetune_vctk_dump.ipynb b/ESPnetEZ/tts_finetune_vctk_dump.ipynb new file mode 100644 index 00000000..9fb239a2 --- /dev/null +++ b/ESPnetEZ/tts_finetune_vctk_dump.ipynb @@ -0,0 +1,4974 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "saK2jMSW8s0T" + }, + "source": [ + "# References\n", + "- [ESPnet repository](https://github.com/espnet/espnet)\n", + "- [ESPnet documentation](https://espnet.github.io/espnet/)\n", + "- [ESPnet-EZ repo](https://github.com/espnet/espnet/tree/master/espnetez)\n", + "- [OWSM ESPnetEZ finetuning demo](https://colab.research.google.com/drive/1Q-J0WUlvAIJbAJ-U_uKQqrvFtHUeWxus)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zc_5YHRXb7lB" + }, + "source": [ + "# Overview\n", + "- Task: Text-to-Speech (TTS)\n", + "- Dataset: [VCTK](https://huggingface.co/datasets/vctk)\n", + "- Model: VITS - [espnet/kan-bayashi_libritts_xvector_vits](https://huggingface.co/espnet/kan-bayashi_libritts_xvector_vits)\n", + "\n", + "This demo includes data preparation, fine-tuning, and inference." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S0LaeC3RzECk" + }, + "source": [ + "# Prepare Environment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install ESPnet and related packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CMdaAIb2WwyV", + "outputId": "11f61287-808c-4e0c-cfa7-2860a5f3e3d9" + }, + "outputs": [], + "source": [ + "!git clone https://github.com/espnet/espnet.git # run only once if you've mounted your google drive and cd to your drive folder" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ABxY3BAWirzI", + "outputId": "0df2a2a7-e0c1-4922-e634-ef5837602593" + }, + "outputs": [], + "source": [ + "!cd espnet && pip install .\n", + "!pip install espnet_model_zoo tensorboard\n", + "!pip install loralib\n", + "!pip install datasets\n", + "!pip install parallel_wavegan" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import espnetez as ez" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define dataset, task, model, output directories" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Kq_g8Wo1jMdL", + "outputId": "f7acff2b-0a8e-44ba-ed9b-fe8ccb6c0c0c" + }, + "outputs": [], + "source": [ + "DATASET_NAME = \"vctk\"\n", + "TASK = \"gan_tts\"\n", + "EXP_DIR = f\"./exp/finetune_{TASK}_{DATASET_NAME}\"\n", + "STATS_DIR = f\"./exp/stats_{DATASET_NAME}\"\n", + "\n", + "PRETRAIN_MODEL = \"espnet/kan-bayashi_libritts_xvector_vits\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tHKwsqGUAT1y" + }, + "source": [ + "# Data Preparation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xow3K7ZXqery" + }, + "source": [ + "## Load Dataset \n", + "Load dataset from ESPnet generated dump files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "DUMP_DIR = f\"dump\"\n", + "data_info = {\n", + " \"speech\": [\"wav.scp\", \"sound\"],\n", + " \"text\": [\"text\", \"text\"],\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "C3pm3nJECzlG" + }, + "source": [ + "# Fine-tuning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set Up Configurations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download a pretrained model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from espnet_model_zoo.downloader import ModelDownloader\n", + "d = ModelDownloader()\n", + "pretrain_downloaded = d.download_and_unpack(PRETRAIN_MODEL)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load configurations of the pretrained model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pretrain_config = ez.config.from_yaml(\n", + " \"tts\",\n", + " pretrain_downloaded['train_config'],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add the pretrained model path to the config file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pretrain_config['model_file'] = pretrain_downloaded['model_file']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Update our configuration for finetuning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lora_config = f\"./config/{DATASET_NAME}/finetune_with_lora.yaml\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EulZ1DBWO0dS" + }, + "outputs": [], + "source": [ + "finetune_config = ez.config.update_finetune_config(\n", + "\tTASK,\n", + "\tpretrain_config,\n", + "\tlora_config\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ga5RP-vEO4b1", + "outputId": "3ff3f500-ea31-4283-9e26-0bf5d8b0e240" + }, + "outputs": [], + "source": [ + "finetune_config['adapter_conf']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "finetune_config['model_file']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "j153lPhscCr2" + }, + "outputs": [], + "source": [ + "finetune_config[\"batch_size\"] = 1\n", + "finetune_config[\"num_workers\"] = 1\n", + "finetune_config[\"max_epoch\"] = 20\n", + "finetune_config[\"num_iters_per_epoch\"] = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GHfDh3W0LPOX", + "outputId": "9d1ad81d-e4c9-43dc-89ee-2538c6b72592" + }, + "outputs": [], + "source": [ + "finetune_config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create a Trainer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the Trainer class\n", + "ngpu = 1\n", + "trainer = ez.Trainer(\n", + " task=TASK,\n", + " train_config=finetune_config,\n", + " train_dump_dir=f\"{DUMP_DIR}/raw/tr_no_dev\",\n", + " valid_dump_dir=f\"{DUMP_DIR}/raw/dev\",\n", + " data_info=data_info,\n", + " output_dir=EXP_DIR,\n", + " stats_dir=STATS_DIR,\n", + " ngpu=ngpu\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer.train_config.train_data_path_and_name_and_type += [\n", + " [f'{DUMP_DIR}/xvector/tr_no_dev/xvector.scp', 'spembs', 'kaldi_ark'],\n", + " ]\n", + "trainer.train_config.valid_data_path_and_name_and_type += [\n", + " [f'{DUMP_DIR}/xvector/dev/xvector.scp', 'spembs', 'kaldi_ark'],\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Collect stats\n", + "To prepare the stats file before training, you can execute the `collect_stats` method. This step is required before the training process and ensuring accurate statistics for the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# trainer.train_config.write_collected_feats = True\n", + "trainer.train_config.normalize = None\n", + "trainer.train_config.pitch_normalize = None\n", + "trainer.train_config.energy_normalize = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PlmDFGMnLZFG", + "outputId": "e0bb1567-baeb-406e-88cf-e10d43d8b925" + }, + "outputs": [], + "source": [ + "trainer.train_config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer.collect_stats()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "trainer.train_config.write_collected_feats = False\n", + "if finetune_config['normalize'] is not None:\n", + " trainer.train_config.normalize = finetune_config['normalize']\n", + " trainer.train_config.normalize_conf['stats_file'] = f'{STATS_DIR}/train/feats_stats.npz'\n", + "if finetune_config['pitch_normalize'] is not None:\n", + " trainer.train_config.pitch_normalize = finetune_config['pitch_normalize']\n", + " trainer.train_config.pitch_normalize_conf['stats_file'] = f'{STATS_DIR}/train/pitch_stats.npz'\n", + "if finetune_config['energy_normalize'] is not None:\n", + " trainer.train_config.energy_normalize = finetune_config['energy_normalize']\n", + " trainer.train_config.energy_normalize_conf['stats_file'] = f'{STATS_DIR}/train/energy_stats.npz'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Sr4uYdZWGHQ2" + }, + "source": [ + "## Training" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "37VhO9u-wTZw", + "outputId": "98973bb1-1bf2-4c0c-d020-b2931016200b" + }, + "outputs": [], + "source": [ + "trainer.train_config" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bOw7U1M4iI8N" + }, + "outputs": [], + "source": [ + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H4phB7dFGxb_" + }, + "source": [ + "# Inference and evaluation\n", + "\n", + "When training is done, we can use the inference API to generate the transcription, but don't forget to apply lora before loading the model!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from espnet.espnet2.bin.tts_inference import inference\n", + "\n", + "output_dir = f\"{EXP_DIR}/inference\"\n", + "inference(\n", + " output_dir=output_dir,\n", + " batch_size=1,\n", + " dtype=\"float32\",\n", + " ngpu=1,\n", + " seed=0,\n", + " num_workers=1,\n", + " log_level=\"INFO\",\n", + " data_path_and_name_and_type=[\n", + " (f'{DUMP_DIR}/raw/dev/text', 'text', 'text'),\n", + " (f'{DUMP_DIR}/xvector/eval1/xvector.scp', 'spembs', 'kaldi_ark'),\n", + " ],\n", + " key_file=None,\n", + " train_config=f\"{EXP_DIR}/config.yaml\",\n", + " model_file=f\"{EXP_DIR}/train.total_count.ave.pth\",\n", + " model_tag=None,\n", + " threshold=0.5,\n", + " minlenratio=0.,\n", + " maxlenratio=10.,\n", + " use_teacher_forcing=False,\n", + " use_att_constraint=False,\n", + " backward_window=1,\n", + " forward_window=3,\n", + " speed_control_alpha=1.0,\n", + " noise_scale=0.667,\n", + " noise_scale_dur=0.8,\n", + " always_fix_seed=False,\n", + " allow_variable_data_keys=False,\n", + " vocoder_config=None,\n", + " vocoder_file=None,\n", + " vocoder_tag=None,\n", + ")" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": 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"markdown", - "metadata": { - "id": "0xmK-IOF0OHS" - }, + "metadata": {}, "source": [ - "# References\n", - "- [ESPnet repository](https://github.com/espnet/espnet)\n", - "- [ESPnet documentation](https://espnet.github.io/espnet/)\n", - "- [ESPnet-EZ repo](https://github.com/espnet/espnet/tree/master/espnetez)\n", - "- [libri100 ESPnetEZ training demo](https://github.com/Masao-Someki/espnet/blob/c7884444ca204049b045f1ba6f2297a34e904374/egsez/asr/libri100.ipynb)\n", - "- [OWSM ESPnetEZ finetuning demo](https://colab.research.google.com/drive/1Q-J0WUlvAIJbAJ-U_uKQqrvFtHUeWxus)" + "# Finetuning VISinger 2 for Singing Voice Synthesis on a New Dataset\n", + "\n", + "This Jupyter notebook provides a step-by-step guide on using the ESPnetEZ module to finetune a pretrained VISinger 2 model. In this demonstration, we will use ESPnet's singing corpora on Hugging Face for the SVS task. This demo covers data preparation, finetuning, inference, and evaluation.\n", + "\n", + "## Overview\n", + "- Task: Singing Voice Synthesis\n", + "- Dataset: *KiSing-v2*\n", + "- Model: *VISinger 2* model trained on *ACE-Opencpop* - [espnet/aceopencpop_svs_visinger2_40singer_pretrain](https://huggingface.co/espnet/aceopencpop_svs_visinger2_40singer_pretrain)\n", + "\n", + "## License Reminder\n", + "Before proceeding, please note that the datasets and models used in this tutorial come with specific licensing terms:\n", + "\n", + "ACE-KiSing Dataset: The ACE-KiSing dataset is distributed under the Creative Commons Attribution Non Commercial 4.0 (CC BY-NC 4.0) license. This means you are free to use, share, and adapt the data, but only for non-commercial purposes. Any commercial use of this dataset is prohibited without explicit permission from the dataset creators.\n", + "\n", + "Pretrained VISinger 2 Model: The VISinger 2 model used in this tutorial is distributed under the Creative Commons Attribution 4.0 (CC BY 4.0) license. This means you can use, modify, and redistribute the model, even for commercial purposes, as long as proper credit is given to the creators." ] }, { "cell_type": "markdown", "metadata": { - "id": "Zc_5YHRXb7lB" + "id": "q7H4TMNw0kDz" }, "source": [ - "# Overview\n", - "- Task: Singing Voice Synthesis\n", - "- Dataset: *KiSing-v2*\n", - "- Model: *VISinger 2* model trained on *ACE-Opencpop* - [espnet/acesinger_opencpop_visinger2_44khz](https://huggingface.co/espnet/acesinger_opencpop_visinger2_44khz)\n", - "\n", - "This demo includes data preparation, finetuning, and inference." + "# Prepare Environment" ] }, { "cell_type": "markdown", - "metadata": { - "id": "q7H4TMNw0kDz" - }, + "metadata": {}, "source": [ - "# Prepare Environment\n", - "## Install ESPnet and related packages" + "## Clone ESPnet's Repository" ] }, { @@ -50,10 +49,16 @@ }, "outputs": [], "source": [ - "# clone ESPnet's repo\n", "!git clone https://github.com/espnet/espnet.git" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install ESPnet and Dependencies" + ] + }, { "cell_type": "code", "execution_count": null, @@ -68,7 +73,6 @@ "source": [ "!cd espnet && pip install .\n", "!pip install espnet_model_zoo tensorboard\n", - "!pip install loralib\n", "!pip install datasets" ] }, @@ -76,7 +80,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Import packages" + "## Import ESPnetEZ" ] }, { @@ -88,29 +92,14 @@ "import espnetez as ez" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Define constants" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "TASK = \"gan_svs\"" - ] - }, { "cell_type": "markdown", "metadata": { "id": "05rJsG030eS8" }, "source": [ - "# Data Preparation" + "# Data Preparation\n", + "We will use ESPnet's ACE-KiSing dataset available on Hugging Face: [espnet/ace-kising-segments](https://huggingface.co/datasets/espnet/ace-kising-segments). Let's begin by loading the dataset, resampling the audio to match the model's requirements, and wrapping it using ESPnetEZtaset." ] }, { @@ -120,7 +109,7 @@ }, "source": [ "## Load dataset\n", - "KiSing-v2 on Hugging Face: [espnet/kising-v2-segments](https://huggingface.co/datasets/espnet/kising-v2-segments)" + "To start, load the ACE-KiSing dataset using the datasets library. " ] }, { @@ -283,7 +272,7 @@ "source": [ "from datasets import load_dataset\n", "\n", - "dataset = load_dataset(\"espnet/kising-v2-segments\", cache_dir=\"data\")\n", + "dataset = load_dataset(\"espnet/ace-kising-segments\", cache_dir=\"cache\")\n", "train_dataset = dataset['train']\n", "valid_dataset = dataset['validation']\n", "test_dataset = dataset['test']" @@ -295,7 +284,7 @@ "id": "xqKDni4zKLPy" }, "source": [ - "The first two instances of the train set:" + "Display the first two instances from the training dataset" ] }, { @@ -320,8 +309,8 @@ "id": "mZIuMy0bVLp9" }, "source": [ - "## Resample audio in accordance with the pretrained model\n", - "Sampling rate is 44.1k in [espnet/acesinger_opencpop_visinger2_44khz](https://huggingface.co/espnet/acesinger_opencpop_visinger2_44khz)\n" + "## Resample Audio\n", + "Resample the audio to a 44.1kHz sampling rate to match the requirements of the pretrained model. For more details, refer to the model's [SVS configuration](https://huggingface.co/espnet/aceopencpop_svs_visinger2_40singer_pretrain#svs-config).\n" ] }, { @@ -345,11 +334,9 @@ }, { "cell_type": "markdown", - "metadata": { - "id": "CiH2uDJstjgz" - }, + "metadata": {}, "source": [ - "## Define data info for constructing a ESPnetEZ dataset" + "## Define Dataset Information" ] }, { @@ -358,6 +345,7 @@ "metadata": {}, "outputs": [], "source": [ + "# Map from speaker names of the KiSing dataset to speaker ids matched with the pretrained model\n", "singer2sid = {\n", " \"barber\": 3,\n", " \"blanca\": 30,\n", @@ -366,7 +354,6 @@ " \"chuming\": 4,\n", " \"crimson\": 1,\n", " \"david\": 28,\n", - " \"dvaid\": 31,\n", " \"ghost\": 27,\n", " \"growl\": 25,\n", " \"hiragi-yuki\": 22,\n", @@ -375,7 +362,7 @@ " \"lien\": 29,\n", " \"liyuan\": 9,\n", " \"luanming\": 21,\n", - " \"luotianyi\": 36,\n", + " \"luotianyi\": 31,\n", " \"namine\": 8,\n", " \"orange\": 12,\n", " \"original\": 32,\n", @@ -392,9 +379,8 @@ " \"xiaomo\": 17,\n", " \"xiaoye\": 14,\n", " \"yanhe\": 33,\n", - " \"yuehao\": 34,\n", - " \"yuezhengling\": 35,\n", - " \"yunhao\": 18\n", + " \"yuezhengling\": 34,\n", + " \"yunhao\": 18,\n", "}" ] }, @@ -407,43 +393,22 @@ "outputs": [], "source": [ "import numpy as np\n", + "\n", + "# Define data mapping functions\n", "data_info = {\n", " \"singing\": lambda d: d['audio']['array'].astype(np.float32),\n", " \"score\": lambda d: (d['tempo'], list(zip(*[d[key] for key in ('note_start_times', 'note_end_times', 'note_lyrics', 'note_midi', 'note_phns')]))),\n", " \"text\": lambda d: d['transcription'],\n", - " \"label\": lambda d: (np.array(list(zip(*[d[key] for key in ('phn_start_time', 'phn_end_time')]))), d['phn']),\n", + " \"label\": lambda d: (np.array(list(zip(*[d[key] for key in ('phn_start_times', 'phn_end_times')]))), d['phns']),\n", " \"sids\": lambda d: np.array([singer2sid[d['singer']]]),\n", "}" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "RqUXRreFzY_y" - }, - "outputs": [], - "source": [ - "inp = next(it)\n", - "inp" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "tNdCOz1bKlLS" - }, - "outputs": [], - "source": [ - "[(k, func(inp))for k, func in data_info.items()]" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Load as ESPnetEZ dataset" + "## Load as ESPnetEZ Dataset" ] }, { @@ -462,16 +427,7 @@ "id": "OMmUTSVVb7lG" }, "source": [ - "# Finetuning" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hgRlRX2G9AgT" - }, - "source": [ - "## Set Up Configurations" + "# Finetuning the Model" ] }, { @@ -480,18 +436,8 @@ "id": "LMFcKDhs7jLJ" }, "source": [ - "Download the [pretrained VISinger2 model on ACE-Opencpop](https://huggingface.co/espnet/acesinger_opencpop_visinger2_44khz) using espnet_model_zoo" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "4DlwQDkeJpVa" - }, - "outputs": [], - "source": [ - "PRETRAIN_MODEL = \"espnet/acesinger_opencpop_visinger2_44khz\"" + "## Download Pretrained VISinger 2 Model\n", + "We'll use ESPnet's model zoo to download the [pretrained VISinger 2 model from the ACE-Opencpop dataset](https://huggingface.co/espnet/aceopencpop_svs_visinger2_40singer_pretrain).\n" ] }, { @@ -503,121 +449,45 @@ "outputs": [], "source": [ "from espnet_model_zoo.downloader import ModelDownloader\n", + "\n", + "PRETRAIN_MODEL = \"espnet/aceopencpop_svs_visinger2_40singer_pretrain\"\n", "d = ModelDownloader()\n", "pretrain_downloaded = d.download_and_unpack(PRETRAIN_MODEL)" ] }, - { - "cell_type": "markdown", - "metadata": { - "id": "m8PSENxm98ZF" - }, - "source": [ - "Load pretrained configuration" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "UfGw3xYh98Id" - }, - "outputs": [], - "source": [ - "# load pretrained config\n", - "pretrain_config = ez.config.from_yaml(\n", - " TASK,\n", - " pretrain_downloaded['train_config'],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "W9j2-JFmJU4v" - }, - "source": [ - "Add the pretrained model path to the config file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "lwpbXF5gMoa8" - }, - "outputs": [], - "source": [ - "pretrain_config['model_file'] = pretrain_downloaded['model_file']" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Kh_T8x-X8iPX" - }, - "source": [ - "Update our configuration for LoRA finetuning" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset_name='kisingv2'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "SkxsJplmb7lH" - }, - "outputs": [], - "source": [ - "# update with lora finetuning config\n", - "finetune_config = ez.config.update_finetune_config(\n", - "\tTASK,\n", - "\tpretrain_config,\n", - "\tf\"./config/{dataset_name}/finetune_with_lora.yaml\"\n", - ")" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Update number of singers" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "finetune_config['svs_conf']['generator_params']['spks'] = 36 + 1" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1s-2p9muWn5N" - }, - "source": [ - "Clear local files in the original config" + "## Configure Fine-Tuning\n", + "Load the pretrained model's configuration and set it up for fine-tuning." ] }, { "cell_type": "code", "execution_count": null, "metadata": { - "id": "bkxkoKFeWs5V" + "id": "UfGw3xYh98Id" }, "outputs": [], "source": [ + "TASK = \"gan_svs\"\n", + "pretrain_config = ez.config.from_yaml(TASK, pretrain_downloaded['train_config'])\n", + "\n", + "# Update the configuration with the downloaded model file path\n", + "pretrain_config['model_file'] = pretrain_downloaded['model_file']\n", + "\n", + "# Modify configuration for fine-tuning\n", + "finetune_config = pretrain_config.copy()\n", + "finetune_config['batch_size'] = 1\n", + "finetune_config[\"num_workers\"] = 1\n", + "finetune_config['max_epoch'] = 40\n", + "finetune_config['output_dir'] = None\n", + "finetune_config[\"save_lora_only\"] = False\n", + "finetune_config[\"num_iters_per_epoch\"] = None\n", + "finetune_config[\"use_preprocessor\"] = True # Use SVS preprocessor for loading the dataset\n", + "\n", + "# Clear the original local file paths in the config\n", "finetune_config['train_data_path_and_name_and_type'] = []\n", "finetune_config['valid_data_path_and_name_and_type'] = []\n", "finetune_config['train_shape_file'] = []\n", @@ -625,52 +495,14 @@ "finetune_config['output_dir'] = None" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use SVS preprocessor in ESPnet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "finetune_config[\"use_preprocessor\"] = True" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uA96KdcKLFuW" - }, - "source": [ - "Customize our fine-tuning config" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "hAtRx7JZLE_w" - }, - "outputs": [], - "source": [ - "finetune_config[\"batch_size\"] = 4\n", - "finetune_config[\"num_workers\"] = 1\n", - "finetune_config[\"max_epoch\"] = 10\n", - "finetune_config[\"num_iters_per_epoch\"] = None" - ] - }, { "cell_type": "markdown", "metadata": { "id": "EPjWSrB78s0Z" }, "source": [ - "## Define a Trainer\n" + "## Initialize Trainer\n", + "Define the trainer for the fine-tuning process.\n" ] }, { @@ -681,19 +513,10 @@ }, "outputs": [], "source": [ + "dataset_name = 'ace-kising'\n", "EXP_DIR = f\"exp/finetune_{dataset_name}_{TASK}\"\n", - "STATS_DIR = f\"exp/stats_{dataset_name}\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "AB1OqYh489Pu" - }, - "outputs": [], - "source": [ - "# Define the Trainer class\n", + "STATS_DIR = f\"exp/stats_{dataset_name}\"\n", + "\n", "trainer = ez.Trainer(\n", " task=TASK,\n", " train_config=finetune_config,\n", @@ -710,8 +533,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Collect stats\n", - "To prepare the stats file before training, you can execute the `collect_stats` method. This step is required before the training process and ensuring accurate statistics for the model." + "## Collect Statistics\n", + "Before training, we need to collect data statistics (e.g., normalization stats)." ] }, { @@ -722,35 +545,19 @@ }, "outputs": [], "source": [ - "# set normalization to None when collecting stats, as we don't have the mean/var yet\n", + "# Temporarily set to None, as we need to collect stats first\n", "trainer.train_config.normalize = None\n", "trainer.train_config.pitch_normalize = None\n", - "trainer.train_config.energy_normalize = None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "trainer.train_config.energy_normalize = None\n", + "\n", "# Collect stats\n", - "trainer.collect_stats()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "_KLr12H6PNXQ" - }, - "outputs": [], - "source": [ - "# set normalization back to the original config\n", + "trainer.collect_stats()\n", + "\n", + "# Restore normalization configs with collected stats\n", "trainer.train_config.normalize = finetune_config['normalize']\n", "trainer.train_config.pitch_normalize = finetune_config['pitch_normalize']\n", "trainer.train_config.normalize_conf['stats_file'] = f'{STATS_DIR}/train/feats_stats.npz'\n", - "trainer.train_config.pitch_normalize_conf['stats_file'] = f'{STATS_DIR}/train/pitch_stats.npz'" + "trainer.train_config.pitch_normalize_conf['stats_file'] = f'{STATS_DIR}/train/pitch_stats.npz'\n" ] }, { @@ -759,7 +566,8 @@ "id": "jroayQlwb7lI" }, "source": [ - "## Start training" + "## Start training\n", + "Now, let's start the fine-tuning process." ] }, { @@ -779,27 +587,11 @@ "id": "dUeu-y28b7lJ" }, "source": [ - "# Inference" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ULAzv_32-YRL" - }, - "outputs": [], - "source": [ - "from espnet2.bin.svs_inference import SingingGenerate" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1TvmC3jGAJPz" - }, - "source": [ - "## Set up model for inference" + "# Inference\n", + "Once the model is fine-tuned, you can generate synthesized singing voice using the test dataset.\n", + "\n", + "## Set Up the Model for Inference\n", + "Load the trained model and prepare for inference" ] }, { @@ -810,9 +602,12 @@ }, "outputs": [], "source": [ + "from espnet2.bin.svs_inference import SingingGenerate\n", + "\n", + "ckpt_name=\"train.total_count.ave_10best\"\n", "m = SingingGenerate(\n", " f\"{EXP_DIR}/config.yaml\",\n", - " f\"{EXP_DIR}/train.total_count.ave_10best.pth\",\n", + " f\"{EXP_DIR}/{ckpt_name}.pth\",\n", ")\n", "\n", "m.model.eval()" @@ -824,7 +619,7 @@ "id": "TWIVS2sPAeHA" }, "source": [ - "## Prepare data for inference" + "## Wrap dataset with ESPnetEZDataset" ] }, { @@ -845,7 +640,7 @@ }, "source": [ "## Run inference with test data\n", - "Here, we'll test the first instance in the test set." + "Here, we will demonstrate how to perform inference using a single data instance from the test dataset." ] }, { @@ -856,11 +651,15 @@ }, "outputs": [], "source": [ - "(key, batch) = next(iter(test_dataset)) # get the first instance\n", + "# Get the first instance\n", + "(key, batch) = next(iter(test_dataset))\n", "\n", + "# Remove unnecessary data from batch\n", "batch.pop('singing')\n", + "batch.pop('text')\n", "sids = batch.pop('sids')\n", "\n", + "# Generate the output\n", "output_dict = m(batch, sids=sids)" ] }, @@ -870,7 +669,7 @@ "id": "m6nwXah5BAru" }, "source": [ - "Write output wav file" + "Save the generated singing voice to a WAV file." ] }, { @@ -889,6 +688,89 @@ " \"PCM_16\",\n", ")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluation\n", + "In this section, we will assess the model's performance based on speaker similarity, Mel-cepstral distortion, the root mean square error (RMSE) of the fundamental frequency (f0), and the Pearson correlation coefficient for f0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from speech_evaluation import speaker_metric, speaker_model_setup, mcd_f0\n", + "from datasets import load_dataset, Audio\n", + "import soundfile as sf\n", + "from pathlib import Path\n", + "\n", + "ckpt_name = \"train.total_count.ave_10best\"\n", + "sr=44100\n", + "\n", + "EXP_DIR = Path(f\"exp/finetune_{dataset_name}_{TASK}\")\n", + "inference_dir = EXP_DIR / f\"inference_test_{ckpt_name}\"\n", + "\n", + "(inference_dir / \"wav\").mkdir(exist_ok=True, parents=True)\n", + "\n", + "test_dataset = load_dataset(\"espnet/ace-kising-segments\", cache_dir=\"cache\", split=\"test\")\n", + "test_dataset = test_dataset.cast_column(\"audio\", Audio(sampling_rate=sr))\n", + "test_dataset = ez.dataset.ESPnetEZDataset(test_dataset, data_info=data_info)\n", + "loader = iter(test_dataset)\n", + "\n", + "\n", + "model = speaker_model_setup()\n", + "spk_similarities = []\n", + "mcd_f0s = []\n", + "f0rmses = []\n", + "f0corrs = []\n", + "for key, batch in loader:\n", + " gt = batch.pop('singing')\n", + " sids = batch.pop('sids')\n", + " batch.pop('text')\n", + " output_dict = m(batch, sids=sids)\n", + " pred = output_dict['wav'].cpu().numpy()\n", + " sf.write(\n", + " f\"{inference_dir}/wav/{key}.wav\",\n", + " pred,\n", + " sr,\n", + " \"PCM_16\",\n", + " )\n", + "\n", + " ret = speaker_metric(model, pred, gt, sr)\n", + " print(ret)\n", + " with open(f\"{inference_dir}/spk_similarity\", \"a\") as f:\n", + " f.write(f\"{ret['spk_similarity']}\\n\")\n", + " spk_similarities.append(ret['spk_similarity'])\n", + " ret = mcd_f0(pred, gt, sr, 1, 800, dtw=True)\n", + " with open(f\"{inference_dir}/mcd_f0\", \"a\") as f:\n", + " f.write(f\"{ret['mcd']}\\n\")\n", + " with open(f\"{inference_dir}/f0rmse\", \"a\") as f:\n", + " f.write(f\"{ret['f0rmse']}\\n\")\n", + " with open(f\"{inference_dir}/f0corr\", \"a\") as f:\n", + " f.write(f\"{ret['f0corr']}\\n\")\n", + " mcd_f0s.append(ret['mcd'])\n", + " f0rmses.append(ret['f0rmse'])\n", + " f0corrs.append(ret['f0corr'])\n", + "\n", + "print(\"Averaged speaker similarity:\", sum(spk_similarities) / len(spk_similarities))\n", + "print(\"Averaged MCD:\", sum(mcd_f0s) / len(mcd_f0s))\n", + "print(\"Averaged F0 RMSE:\", sum(f0rmses) / len(f0rmses))\n", + "print(\"Averaged F0 Corr:\", sum(f0corrs) / len(f0corrs))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "[1] S. Someki, K. Choi, S. Arora, W. Chen, S. Cornell, J. Han, Y. Peng, J. Shi, V. Srivastav, and S. Watanabe, “ESPnet-EZ: Python-only ESPnet for Easy Fine-tuning and Integration,” *arXiv preprint* arXiv:2409.09506, 2024.\n", + "\n", + "[2] J. Shi, Y. Lin, X. Bai, K. Zhang, Y. Wu, Y. Tang, Y. Yu, Q. Jin, and S. Watanabe, “Singing Voice Data Scaling-up: An Introduction to ACE-Opencpop and ACE-KiSing,” *arXiv preprint* arXiv:2401.17619, 2024." + ] } ], "metadata": { From 24492507726abd20de9931bf198fa4fbb936463d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CJionghao=E2=80=9D?= Date: Tue, 24 Sep 2024 23:22:12 -0400 Subject: [PATCH 4/6] Formatted SVS demo --- ...v2.ipynb => svs_finetune_ace-kising.ipynb} | 84 ++++++++++--------- 1 file changed, 43 insertions(+), 41 deletions(-) rename ESPnetEZ/{svs_finetune_kising_v2.ipynb => svs_finetune_ace-kising.ipynb} (98%) diff --git a/ESPnetEZ/svs_finetune_kising_v2.ipynb b/ESPnetEZ/svs_finetune_ace-kising.ipynb similarity index 98% rename from ESPnetEZ/svs_finetune_kising_v2.ipynb rename to ESPnetEZ/svs_finetune_ace-kising.ipynb index 6469ae13..f926ae03 100644 --- a/ESPnetEZ/svs_finetune_kising_v2.ipynb +++ b/ESPnetEZ/svs_finetune_ace-kising.ipynb @@ -4,21 +4,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Finetuning VISinger 2 for Singing Voice Synthesis on a New Dataset\n", + "# Fine-Tuning VISinger 2 for Singing Voice Synthesis on a New Dataset\n", "\n", - "This Jupyter notebook provides a step-by-step guide on using the ESPnetEZ module to finetune a pretrained VISinger 2 model. In this demonstration, we will use ESPnet's singing corpora on Hugging Face for the SVS task. This demo covers data preparation, finetuning, inference, and evaluation.\n", + "This Jupyter notebook provides a step-by-step guide on using the ESPnetEZ module to fine-tune a pretrained VISinger 2 model. In this demonstration, we will use ESPnet's singing corpora on Hugging Face for the SVS task. This demo covers data preparation, fine-tuning, inference, and evaluation.\n", "\n", "## Overview\n", "- Task: Singing Voice Synthesis\n", - "- Dataset: *KiSing-v2*\n", + "- Dataset: *ACE-KiSing*\n", "- Model: *VISinger 2* model trained on *ACE-Opencpop* - [espnet/aceopencpop_svs_visinger2_40singer_pretrain](https://huggingface.co/espnet/aceopencpop_svs_visinger2_40singer_pretrain)\n", "\n", "## License Reminder\n", "Before proceeding, please note that the datasets and models used in this tutorial come with specific licensing terms:\n", "\n", - "ACE-KiSing Dataset: The ACE-KiSing dataset is distributed under the Creative Commons Attribution Non Commercial 4.0 (CC BY-NC 4.0) license. This means you are free to use, share, and adapt the data, but only for non-commercial purposes. Any commercial use of this dataset is prohibited without explicit permission from the dataset creators.\n", + "- **ACE-KiSing Dataset:** The ACE-KiSing dataset is distributed under the Creative Commons Attribution Non Commercial 4.0 (CC BY-NC 4.0) license. This means you are free to use, share, and adapt the data, but only for non-commercial purposes. Any commercial use of this dataset is prohibited without explicit permission from the dataset creators.\n", "\n", - "Pretrained VISinger 2 Model: The VISinger 2 model used in this tutorial is distributed under the Creative Commons Attribution 4.0 (CC BY 4.0) license. This means you can use, modify, and redistribute the model, even for commercial purposes, as long as proper credit is given to the creators." + "- **Pretrained VISinger 2 Model:** The VISinger 2 model used in this tutorial is distributed under the Creative Commons Attribution 4.0 (CC BY 4.0) license. This means you can use, modify, and redistribute the model, even for commercial purposes, as long as proper credit is given to the creators." ] }, { @@ -273,9 +273,9 @@ "from datasets import load_dataset\n", "\n", "dataset = load_dataset(\"espnet/ace-kising-segments\", cache_dir=\"cache\")\n", - "train_dataset = dataset['train']\n", - "valid_dataset = dataset['validation']\n", - "test_dataset = dataset['test']" + "train_dataset = dataset[\"train\"]\n", + "valid_dataset = dataset[\"validation\"]\n", + "test_dataset = dataset[\"test\"]" ] }, { @@ -327,6 +327,7 @@ "outputs": [], "source": [ "from datasets import Audio\n", + "\n", "train_dataset = train_dataset.cast_column(\"audio\", Audio(sampling_rate=44100))\n", "valid_dataset = valid_dataset.cast_column(\"audio\", Audio(sampling_rate=44100))\n", "test_dataset = test_dataset.cast_column(\"audio\", Audio(sampling_rate=44100))" @@ -427,7 +428,7 @@ "id": "OMmUTSVVb7lG" }, "source": [ - "# Finetuning the Model" + "# Fine-Tuning" ] }, { @@ -472,27 +473,26 @@ "outputs": [], "source": [ "TASK = \"gan_svs\"\n", - "pretrain_config = ez.config.from_yaml(TASK, pretrain_downloaded['train_config'])\n", + "pretrain_config = ez.config.from_yaml(TASK, pretrain_downloaded[\"train_config\"])\n", "\n", "# Update the configuration with the downloaded model file path\n", - "pretrain_config['model_file'] = pretrain_downloaded['model_file']\n", + "pretrain_config[\"model_file\"] = pretrain_downloaded[\"model_file\"]\n", "\n", "# Modify configuration for fine-tuning\n", "finetune_config = pretrain_config.copy()\n", - "finetune_config['batch_size'] = 1\n", + "finetune_config[\"batch_size\"] = 1\n", "finetune_config[\"num_workers\"] = 1\n", - "finetune_config['max_epoch'] = 40\n", - "finetune_config['output_dir'] = None\n", + "finetune_config[\"max_epoch\"] = 40\n", "finetune_config[\"save_lora_only\"] = False\n", "finetune_config[\"num_iters_per_epoch\"] = None\n", - "finetune_config[\"use_preprocessor\"] = True # Use SVS preprocessor for loading the dataset\n", + "finetune_config[\"use_preprocessor\"] = True # Use SVS preprocessor for loading the dataset\n", "\n", "# Clear the original local file paths in the config\n", - "finetune_config['train_data_path_and_name_and_type'] = []\n", - "finetune_config['valid_data_path_and_name_and_type'] = []\n", - "finetune_config['train_shape_file'] = []\n", - "finetune_config['valid_shape_file'] = []\n", - "finetune_config['output_dir'] = None" + "finetune_config[\"train_data_path_and_name_and_type\"] = []\n", + "finetune_config[\"valid_data_path_and_name_and_type\"] = []\n", + "finetune_config[\"train_shape_file\"] = []\n", + "finetune_config[\"valid_shape_file\"] = []\n", + "finetune_config[\"output_dir\"] = None" ] }, { @@ -513,7 +513,7 @@ }, "outputs": [], "source": [ - "dataset_name = 'ace-kising'\n", + "dataset_name = \"ace-kising\"\n", "EXP_DIR = f\"exp/finetune_{dataset_name}_{TASK}\"\n", "STATS_DIR = f\"exp/stats_{dataset_name}\"\n", "\n", @@ -554,10 +554,10 @@ "trainer.collect_stats()\n", "\n", "# Restore normalization configs with collected stats\n", - "trainer.train_config.normalize = finetune_config['normalize']\n", - "trainer.train_config.pitch_normalize = finetune_config['pitch_normalize']\n", - "trainer.train_config.normalize_conf['stats_file'] = f'{STATS_DIR}/train/feats_stats.npz'\n", - "trainer.train_config.pitch_normalize_conf['stats_file'] = f'{STATS_DIR}/train/pitch_stats.npz'\n" + "trainer.train_config.normalize = finetune_config[\"normalize\"]\n", + "trainer.train_config.pitch_normalize = finetune_config[\"pitch_normalize\"]\n", + "trainer.train_config.normalize_conf[\"stats_file\"] = f\"{STATS_DIR}/train/feats_stats.npz\"\n", + "trainer.train_config.pitch_normalize_conf[\"stats_file\"] = f\"{STATS_DIR}/train/pitch_stats.npz\"" ] }, { @@ -566,7 +566,7 @@ "id": "jroayQlwb7lI" }, "source": [ - "## Start training\n", + "## Start Training\n", "Now, let's start the fine-tuning process." ] }, @@ -604,7 +604,7 @@ "source": [ "from espnet2.bin.svs_inference import SingingGenerate\n", "\n", - "ckpt_name=\"train.total_count.ave_10best\"\n", + "ckpt_name = \"train.total_count.ave_10best\"\n", "m = SingingGenerate(\n", " f\"{EXP_DIR}/config.yaml\",\n", " f\"{EXP_DIR}/{ckpt_name}.pth\",\n", @@ -655,9 +655,9 @@ "(key, batch) = next(iter(test_dataset))\n", "\n", "# Remove unnecessary data from batch\n", - "batch.pop('singing')\n", - "batch.pop('text')\n", - "sids = batch.pop('sids')\n", + "batch.pop(\"singing\")\n", + "batch.pop(\"text\")\n", + "sids = batch.pop(\"sids\")\n", "\n", "# Generate the output\n", "output_dict = m(batch, sids=sids)" @@ -681,6 +681,7 @@ "outputs": [], "source": [ "import soundfile as sf\n", + "\n", "sf.write(\n", " f\"{EXP_DIR}/{key}.wav\",\n", " output_dict[\"wav\"].cpu().numpy(),\n", @@ -709,14 +710,16 @@ "from pathlib import Path\n", "\n", "ckpt_name = \"train.total_count.ave_10best\"\n", - "sr=44100\n", + "sr = 44100\n", "\n", "EXP_DIR = Path(f\"exp/finetune_{dataset_name}_{TASK}\")\n", "inference_dir = EXP_DIR / f\"inference_test_{ckpt_name}\"\n", "\n", "(inference_dir / \"wav\").mkdir(exist_ok=True, parents=True)\n", "\n", - "test_dataset = load_dataset(\"espnet/ace-kising-segments\", cache_dir=\"cache\", split=\"test\")\n", + "test_dataset = load_dataset(\n", + " \"espnet/ace-kising-segments\", cache_dir=\"cache\", split=\"test\"\n", + ")\n", "test_dataset = test_dataset.cast_column(\"audio\", Audio(sampling_rate=sr))\n", "test_dataset = ez.dataset.ESPnetEZDataset(test_dataset, data_info=data_info)\n", "loader = iter(test_dataset)\n", @@ -728,11 +731,11 @@ "f0rmses = []\n", "f0corrs = []\n", "for key, batch in loader:\n", - " gt = batch.pop('singing')\n", - " sids = batch.pop('sids')\n", - " batch.pop('text')\n", + " gt = batch.pop(\"singing\")\n", + " sids = batch.pop(\"sids\")\n", + " batch.pop(\"text\")\n", " output_dict = m(batch, sids=sids)\n", - " pred = output_dict['wav'].cpu().numpy()\n", + " pred = output_dict[\"wav\"].cpu().numpy()\n", " sf.write(\n", " f\"{inference_dir}/wav/{key}.wav\",\n", " pred,\n", @@ -741,10 +744,9 @@ " )\n", "\n", " ret = speaker_metric(model, pred, gt, sr)\n", - " print(ret)\n", " with open(f\"{inference_dir}/spk_similarity\", \"a\") as f:\n", " f.write(f\"{ret['spk_similarity']}\\n\")\n", - " spk_similarities.append(ret['spk_similarity'])\n", + " spk_similarities.append(ret[\"spk_similarity\"])\n", " ret = mcd_f0(pred, gt, sr, 1, 800, dtw=True)\n", " with open(f\"{inference_dir}/mcd_f0\", \"a\") as f:\n", " f.write(f\"{ret['mcd']}\\n\")\n", @@ -752,9 +754,9 @@ " f.write(f\"{ret['f0rmse']}\\n\")\n", " with open(f\"{inference_dir}/f0corr\", \"a\") as f:\n", " f.write(f\"{ret['f0corr']}\\n\")\n", - " mcd_f0s.append(ret['mcd'])\n", - " f0rmses.append(ret['f0rmse'])\n", - " f0corrs.append(ret['f0corr'])\n", + " mcd_f0s.append(ret[\"mcd\"])\n", + " f0rmses.append(ret[\"f0rmse\"])\n", + " f0corrs.append(ret[\"f0corr\"])\n", "\n", "print(\"Averaged speaker similarity:\", sum(spk_similarities) / len(spk_similarities))\n", "print(\"Averaged MCD:\", sum(mcd_f0s) / len(mcd_f0s))\n", From eb02eb0f1c613d419e3ea943d8fb401823824f98 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CJionghao=E2=80=9D?= Date: Tue, 24 Sep 2024 23:33:09 -0400 Subject: [PATCH 5/6] Updated TTS finetuning demo with full-finetuning; Removed SVS and TTS LoRA finetuning config files --- .../config/kisingv2/finetune_with_lora.yaml | 17 - ESPnetEZ/config/vctk/finetune_with_lora.yaml | 15 - ESPnetEZ/tts_finetune_vctk_dump.ipynb | 439 +++++++----------- 3 files changed, 174 insertions(+), 297 deletions(-) delete mode 100644 ESPnetEZ/config/kisingv2/finetune_with_lora.yaml delete mode 100644 ESPnetEZ/config/vctk/finetune_with_lora.yaml diff --git a/ESPnetEZ/config/kisingv2/finetune_with_lora.yaml b/ESPnetEZ/config/kisingv2/finetune_with_lora.yaml deleted file mode 100644 index 4d8811c3..00000000 --- a/ESPnetEZ/config/kisingv2/finetune_with_lora.yaml +++ /dev/null @@ -1,17 +0,0 @@ -use_adapter: true -adapter: lora -save_strategy: adapter -adapter_conf: - rank: 4 - alpha: 4 - dropout_rate: 0.1 - target_modules: - - self_attn.linear_q - - self_attn.linear_k - - self_attn.linear_v - - self_attn.linear_out - - emb_phone - - emb_pitch - - emb_slur - - global_emb -freeze_param: ["svs.generator"] \ No newline at end of file diff --git a/ESPnetEZ/config/vctk/finetune_with_lora.yaml b/ESPnetEZ/config/vctk/finetune_with_lora.yaml deleted file mode 100644 index ce9262d0..00000000 --- a/ESPnetEZ/config/vctk/finetune_with_lora.yaml +++ /dev/null @@ -1,15 +0,0 @@ -use_adapter: true -adapter: lora -save_strategy: adapter -adapter_conf: - rank: 4 - alpha: 4 - dropout_rate: 0.1 - target_modules: - - self_attn.linear_q - - self_attn.linear_k - - self_attn.linear_v - - self_attn.linear_out - - emb - -freeze_param: ["tts.generator"] \ No newline at end of file diff --git a/ESPnetEZ/tts_finetune_vctk_dump.ipynb b/ESPnetEZ/tts_finetune_vctk_dump.ipynb index 9fb239a2..f8f57edc 100644 --- a/ESPnetEZ/tts_finetune_vctk_dump.ipynb +++ b/ESPnetEZ/tts_finetune_vctk_dump.ipynb @@ -1,30 +1,23 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "saK2jMSW8s0T" - }, - "source": [ - "# References\n", - "- [ESPnet repository](https://github.com/espnet/espnet)\n", - "- [ESPnet documentation](https://espnet.github.io/espnet/)\n", - "- [ESPnet-EZ repo](https://github.com/espnet/espnet/tree/master/espnetez)\n", - "- [OWSM ESPnetEZ finetuning demo](https://colab.research.google.com/drive/1Q-J0WUlvAIJbAJ-U_uKQqrvFtHUeWxus)\n" - ] - }, { "cell_type": "markdown", "metadata": { "id": "Zc_5YHRXb7lB" }, "source": [ - "# Overview\n", + "# Fine-Tuning VITS for Text-to-Speech Synthesis on a New Dataset\n", + "In this tutorial, we will guide you through the process of performing text-to-speech (TTS) synthesis by fine-tuning the VITS model on the VCTK dataset. This demo covers data preparation from dump files, model fine-tuning, inference, and evaluation.\n", + "\n", + "## Overview\n", "- Task: Text-to-Speech (TTS)\n", - "- Dataset: [VCTK](https://huggingface.co/datasets/vctk)\n", + "- Dataset: [VCTK](http://www.udialogue.org/download/cstr-vctk-corpus.html)\n", "- Model: VITS - [espnet/kan-bayashi_libritts_xvector_vits](https://huggingface.co/espnet/kan-bayashi_libritts_xvector_vits)\n", "\n", - "This demo includes data preparation, fine-tuning, and inference." + "## License Reminder\n", + "Before proceeding, please note that the dataset and model used in this tutorial come with specific licensing terms:\n", + "- **VCTK Corpus:** Licensed under the Open Data Commons Attribution License (ODC-By) v1.0.\n", + "- **Model:** The pretrained VITS model is under the Creative Commons Attribution 4.0 License.\n" ] }, { @@ -40,48 +33,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Install ESPnet and related packages" + "## Clone ESPnet's Repository" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "CMdaAIb2WwyV", - "outputId": "11f61287-808c-4e0c-cfa7-2860a5f3e3d9" - }, - "outputs": [], - "source": [ - "!git clone https://github.com/espnet/espnet.git # run only once if you've mounted your google drive and cd to your drive folder" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ABxY3BAWirzI", - "outputId": "0df2a2a7-e0c1-4922-e634-ef5837602593" - }, + "metadata": {}, "outputs": [], "source": [ - "!cd espnet && pip install .\n", - "!pip install espnet_model_zoo tensorboard\n", - "!pip install loralib\n", - "!pip install datasets\n", - "!pip install parallel_wavegan" + "!git clone https://github.com/espnet/espnet.git" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Import packages" + "## Install ESPnet and Dependencies" ] }, { @@ -90,34 +58,25 @@ "metadata": {}, "outputs": [], "source": [ - "import espnetez as ez" + "!cd espnet && pip install .\n", + "!pip install espnet_model_zoo tensorboard\n", + "!pip install datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Define dataset, task, model, output directories" + "## Import ESPnetEZ" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Kq_g8Wo1jMdL", - "outputId": "f7acff2b-0a8e-44ba-ed9b-fe8ccb6c0c0c" - }, + "metadata": {}, "outputs": [], "source": [ - "DATASET_NAME = \"vctk\"\n", - "TASK = \"gan_tts\"\n", - "EXP_DIR = f\"./exp/finetune_{TASK}_{DATASET_NAME}\"\n", - "STATS_DIR = f\"./exp/stats_{DATASET_NAME}\"\n", - "\n", - "PRETRAIN_MODEL = \"espnet/kan-bayashi_libritts_xvector_vits\"" + "import espnetez as ez" ] }, { @@ -135,8 +94,7 @@ "id": "xow3K7ZXqery" }, "source": [ - "## Load Dataset \n", - "Load dataset from ESPnet generated dump files" + "In this tutorial, we will use ESPnet-generated dump files as our inputs. Set up the directory where your processed dump folder is stored." ] }, { @@ -158,21 +116,15 @@ "id": "C3pm3nJECzlG" }, "source": [ - "# Fine-tuning" + "# Fine-Tuning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Set Up Configurations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Download a pretrained model" + "## Download Pretrained VITS Model\n", + "We'll use ESPnet's model zoo to download the [pretrained VITS model from the LibriTTS corpus](https://huggingface.co/espnet/kan-bayashi_libritts_xvector_vits).\n" ] }, { @@ -182,6 +134,8 @@ "outputs": [], "source": [ "from espnet_model_zoo.downloader import ModelDownloader\n", + "\n", + "PRETRAIN_MODEL = \"espnet/kan-bayashi_libritts_xvector_vits\"\n", "d = ModelDownloader()\n", "pretrain_downloaded = d.download_and_unpack(PRETRAIN_MODEL)" ] @@ -190,26 +144,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Load configurations of the pretrained model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pretrain_config = ez.config.from_yaml(\n", - " \"tts\",\n", - " pretrain_downloaded['train_config'],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Add the pretrained model path to the config file" + "## Configure Fine-Tuning\n", + "\n", + "Load the pretrained model's configuration and set it up for fine-tuning." ] }, { @@ -218,62 +155,40 @@ "metadata": {}, "outputs": [], "source": [ - "pretrain_config['model_file'] = pretrain_downloaded['model_file']" + "TASK = \"gan_tts\"\n", + "\n", + "pretrain_config = ez.config.from_yaml(TASK, pretrain_downloaded[\"train_config\"])\n", + "\n", + "# Update the configuration with the downloaded model file path\n", + "pretrain_config[\"model_file\"] = pretrain_downloaded[\"model_file\"]\n", + "\n", + "# Modify configuration for fine-tuning\n", + "finetune_config = pretrain_config.copy()\n", + "finetune_config[\"batch_size\"] = 1\n", + "finetune_config[\"num_workers\"] = 1\n", + "finetune_config[\"max_epoch\"] = 100\n", + "finetune_config[\"batch_bins\"] = 500000\n", + "finetune_config[\"num_iters_per_epoch\"] = None\n", + "finetune_config[\"generator_first\"] = True\n", + "\n", + "# Disable distributed training\n", + "finetune_config[\"distributed\"] = False\n", + "finetune_config[\"multiprocessing_distributed\"] = False\n", + "finetune_config[\"dist_world_size\"] = None\n", + "finetune_config[\"dist_rank\"] = None\n", + "finetune_config[\"local_rank\"] = None\n", + "finetune_config[\"dist_master_addr\"] = None\n", + "finetune_config[\"dist_master_port\"] = None\n", + "finetune_config[\"dist_launcher\"] = None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Update our configuration for finetuning" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lora_config = f\"./config/{DATASET_NAME}/finetune_with_lora.yaml\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "EulZ1DBWO0dS" - }, - "outputs": [], - "source": [ - "finetune_config = ez.config.update_finetune_config(\n", - "\tTASK,\n", - "\tpretrain_config,\n", - "\tlora_config\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ga5RP-vEO4b1", - "outputId": "3ff3f500-ea31-4283-9e26-0bf5d8b0e240" - }, - "outputs": [], - "source": [ - "finetune_config['adapter_conf']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "finetune_config['model_file']" + "## Initialize Trainer\n", + "\n", + "Define the trainer for the fine-tuning process." ] }, { @@ -284,42 +199,11 @@ }, "outputs": [], "source": [ - "finetune_config[\"batch_size\"] = 1\n", - "finetune_config[\"num_workers\"] = 1\n", - "finetune_config[\"max_epoch\"] = 20\n", - "finetune_config[\"num_iters_per_epoch\"] = None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "GHfDh3W0LPOX", - "outputId": "9d1ad81d-e4c9-43dc-89ee-2538c6b72592" - }, - "outputs": [], - "source": [ - "finetune_config" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a Trainer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the Trainer class\n", + "DATASET_NAME = \"vctk\"\n", + "EXP_DIR = f\"./exp/finetune_{TASK}_{DATASET_NAME}\"\n", + "STATS_DIR = f\"./exp/stats_{DATASET_NAME}\"\n", "ngpu = 1\n", + "\n", "trainer = ez.Trainer(\n", " task=TASK,\n", " train_config=finetune_config,\n", @@ -328,30 +212,25 @@ " data_info=data_info,\n", " output_dir=EXP_DIR,\n", " stats_dir=STATS_DIR,\n", - " ngpu=ngpu\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + " ngpu=ngpu,\n", + ")\n", + "\n", + "# Add the xvector paths to the configuration\n", "trainer.train_config.train_data_path_and_name_and_type += [\n", - " [f'{DUMP_DIR}/xvector/tr_no_dev/xvector.scp', 'spembs', 'kaldi_ark'],\n", - " ]\n", + " [f\"{DUMP_DIR}/xvector/tr_no_dev/xvector.scp\", \"spembs\", \"kaldi_ark\"],\n", + "]\n", "trainer.train_config.valid_data_path_and_name_and_type += [\n", - " [f'{DUMP_DIR}/xvector/dev/xvector.scp', 'spembs', 'kaldi_ark'],\n", - " ]" + " [f\"{DUMP_DIR}/xvector/dev/xvector.scp\", \"spembs\", \"kaldi_ark\"],\n", + "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Collect stats\n", - "To prepare the stats file before training, you can execute the `collect_stats` method. This step is required before the training process and ensuring accurate statistics for the model." + "## Collect Statistics\n", + "\n", + "Before training, we need to collect data statistics (e.g., normalization stats)." ] }, { @@ -360,76 +239,40 @@ "metadata": {}, "outputs": [], "source": [ - "# trainer.train_config.write_collected_feats = True\n", + "# Temporarily set to None, as we need to collect stats first\n", "trainer.train_config.normalize = None\n", "trainer.train_config.pitch_normalize = None\n", - "trainer.train_config.energy_normalize = None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "PlmDFGMnLZFG", - "outputId": "e0bb1567-baeb-406e-88cf-e10d43d8b925" - }, - "outputs": [], - "source": [ - "trainer.train_config" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trainer.collect_stats()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "trainer.train_config.energy_normalize = None\n", + "\n", + "# Collect stats\n", + "trainer.collect_stats()\n", + "\n", + "# Restore normalization configs with collected stats\n", "trainer.train_config.write_collected_feats = False\n", - "if finetune_config['normalize'] is not None:\n", - " trainer.train_config.normalize = finetune_config['normalize']\n", - " trainer.train_config.normalize_conf['stats_file'] = f'{STATS_DIR}/train/feats_stats.npz'\n", - "if finetune_config['pitch_normalize'] is not None:\n", - " trainer.train_config.pitch_normalize = finetune_config['pitch_normalize']\n", - " trainer.train_config.pitch_normalize_conf['stats_file'] = f'{STATS_DIR}/train/pitch_stats.npz'\n", - "if finetune_config['energy_normalize'] is not None:\n", - " trainer.train_config.energy_normalize = finetune_config['energy_normalize']\n", - " trainer.train_config.energy_normalize_conf['stats_file'] = f'{STATS_DIR}/train/energy_stats.npz'" + "if finetune_config[\"normalize\"] is not None:\n", + " trainer.train_config.normalize = finetune_config[\"normalize\"]\n", + " trainer.train_config.normalize_conf[\"stats_file\"] = (\n", + " f\"{STATS_DIR}/train/feats_stats.npz\"\n", + " )\n", + "if finetune_config[\"pitch_normalize\"] is not None:\n", + " trainer.train_config.pitch_normalize = finetune_config[\"pitch_normalize\"]\n", + " trainer.train_config.pitch_normalize_conf[\"stats_file\"] = (\n", + " f\"{STATS_DIR}/train/pitch_stats.npz\"\n", + " )\n", + "if finetune_config[\"energy_normalize\"] is not None:\n", + " trainer.train_config.energy_normalize = finetune_config[\"energy_normalize\"]\n", + " trainer.train_config.energy_normalize_conf[\"stats_file\"] = (\n", + " f\"{STATS_DIR}/train/energy_stats.npz\"\n", + " )" ] }, { "cell_type": "markdown", - "metadata": { - "id": "Sr4uYdZWGHQ2" - }, - "source": [ - "## Training" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "37VhO9u-wTZw", - "outputId": "98973bb1-1bf2-4c0c-d020-b2931016200b" - }, - "outputs": [], + "metadata": {}, "source": [ - "trainer.train_config" + "## Start Training\n", + "\n", + "Now, let's start the fine-tuning process." ] }, { @@ -449,9 +292,9 @@ "id": "H4phB7dFGxb_" }, "source": [ - "# Inference and evaluation\n", + "# Inference\n", "\n", - "When training is done, we can use the inference API to generate the transcription, but don't forget to apply lora before loading the model!" + "When training is done, we can use the inference API to synthesize audio from the test set." ] }, { @@ -460,28 +303,32 @@ "metadata": {}, "outputs": [], "source": [ - "from espnet.espnet2.bin.tts_inference import inference\n", + "from espnet2.bin.tts_inference import inference\n", + "\n", + "ckpt_name = \"train.total_count.ave_10best\"\n", + "inference_folder = f\"{EXP_DIR}/inference_{ckpt_name}\"\n", + "model_file = f\"{EXP_DIR}/{ckpt_name}.pth\"\n", "\n", - "output_dir = f\"{EXP_DIR}/inference\"\n", "inference(\n", - " output_dir=output_dir,\n", + " output_dir=inference_folder,\n", " batch_size=1,\n", " dtype=\"float32\",\n", - " ngpu=1,\n", + " ngpu=0,\n", " seed=0,\n", " num_workers=1,\n", " log_level=\"INFO\",\n", " data_path_and_name_and_type=[\n", - " (f'{DUMP_DIR}/raw/dev/text', 'text', 'text'),\n", - " (f'{DUMP_DIR}/xvector/eval1/xvector.scp', 'spembs', 'kaldi_ark'),\n", + " (f\"{DUMP_DIR}/raw/eval1/text\", \"text\", \"text\"),\n", + " (f\"{DUMP_DIR}/raw/eval1/wav.scp\", \"speech\", \"sound\"),\n", + " (f\"{DUMP_DIR}/xvector/eval1/xvector.scp\", \"spembs\", \"kaldi_ark\"),\n", " ],\n", " key_file=None,\n", " train_config=f\"{EXP_DIR}/config.yaml\",\n", - " model_file=f\"{EXP_DIR}/train.total_count.ave.pth\",\n", + " model_file=model_file,\n", " model_tag=None,\n", " threshold=0.5,\n", - " minlenratio=0.,\n", - " maxlenratio=10.,\n", + " minlenratio=0.0,\n", + " maxlenratio=10.0,\n", " use_teacher_forcing=False,\n", " use_att_constraint=False,\n", " backward_window=1,\n", @@ -496,6 +343,68 @@ " vocoder_tag=None,\n", ")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluation\n", + "In this section, we will assess the model's performance based on speaker similarity, Mel-cepstral distortion, the root mean square error (RMSE) of the fundamental frequency (f0), and the Pearson correlation coefficient for f0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import soundfile as sf\n", + "from speech_evaluation import speaker_metric, speaker_model_setup, mcd_f0\n", + "\n", + "gt_wav_scp = f\"{DUMP_DIR}/raw/eval1/wav.scp\"\n", + "\n", + "model = speaker_model_setup()\n", + "spk_similarities = []\n", + "mcd_f0s = []\n", + "f0rmses = []\n", + "f0corrs = []\n", + "\n", + "with open(gt_wav_scp, \"r\") as f:\n", + " for line in f:\n", + " key, path = line.strip().split()\n", + " gt, sr = sf.read(path)\n", + " pred, sr = sf.read(f\"{inference_folder}/wav/{key}.wav\")\n", + " ret = speaker_metric(model, pred, gt, sr)\n", + " with open(f\"{inference_folder}/spk_similarity\", \"a\") as f:\n", + " f.write(f\"{ret['spk_similarity']}\\n\")\n", + " spk_similarities.append(ret[\"spk_similarity\"])\n", + " ret = mcd_f0(pred, gt, sr, 1, 800, dtw=True)\n", + " with open(f\"{inference_folder}/mcd_f0\", \"a\") as f:\n", + " f.write(f\"{ret['mcd']}\\n\")\n", + " with open(f\"{inference_folder}/f0rmse\", \"a\") as f:\n", + " f.write(f\"{ret['f0rmse']}\\n\")\n", + " with open(f\"{inference_folder}/f0corr\", \"a\") as f:\n", + " f.write(f\"{ret['f0corr']}\\n\")\n", + " mcd_f0s.append(ret[\"mcd\"])\n", + " f0rmses.append(ret[\"f0rmse\"])\n", + " f0corrs.append(ret[\"f0corr\"])\n", + "\n", + "print(\"Averaged speaker similarity:\", sum(spk_similarities) / len(spk_similarities))\n", + "print(\"Averaged MCD:\", sum(mcd_f0s) / len(mcd_f0s))\n", + "print(\"Averaged F0 RMSE:\", sum(f0rmses) / len(f0rmses))\n", + "print(\"Averaged F0 Corr:\", sum(f0corrs) / len(f0corrs))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# References\n", + "\n", + "[1] S. Someki, K. Choi, S. Arora, W. Chen, S. Cornell, J. Han, Y. Peng, J. Shi, V. Srivastav, and S. Watanabe, “ESPnet-EZ: Python-only ESPnet for Easy Fine-tuning and Integration,” *arXiv preprint* arXiv:2409.09506, 2024.\n", + "\n", + "[2] C. Veaux, J. Yamagishi, and K. MacDonald, “CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit,” University of Edinburgh, The Centre for Speech Technology Research (CSTR), 2017. [Sound]. https://doi.org/10.7488/ds/1994." + ] } ], "metadata": { From 33ffce6a1fbbf172bfb53711b63e102ca55fec28 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E2=80=9CJionghao=E2=80=9D?= Date: Tue, 24 Sep 2024 23:35:31 -0400 Subject: [PATCH 6/6] Align with espnetez notebook folder structure --- .../SVS_finetune_ace-kising.ipynb} | 0 .../TTS_finetune_vctk_dump.ipynb} | 0 2 files changed, 0 insertions(+), 0 deletions(-) rename ESPnetEZ/{svs_finetune_ace-kising.ipynb => SVS/SVS_finetune_ace-kising.ipynb} (100%) rename ESPnetEZ/{tts_finetune_vctk_dump.ipynb => TTS/TTS_finetune_vctk_dump.ipynb} (100%) diff --git a/ESPnetEZ/svs_finetune_ace-kising.ipynb b/ESPnetEZ/SVS/SVS_finetune_ace-kising.ipynb similarity index 100% rename from ESPnetEZ/svs_finetune_ace-kising.ipynb rename to ESPnetEZ/SVS/SVS_finetune_ace-kising.ipynb diff --git a/ESPnetEZ/tts_finetune_vctk_dump.ipynb b/ESPnetEZ/TTS/TTS_finetune_vctk_dump.ipynb similarity index 100% rename from ESPnetEZ/tts_finetune_vctk_dump.ipynb rename to ESPnetEZ/TTS/TTS_finetune_vctk_dump.ipynb