Merge pull request #1920 from JRMeyer/transfer-learning-notebook

Fix config checkpoint handling and add notebook

notebooks/README.md

@@ -0,0 +1,4 @@
+# Python Notebooks for 🐸 STT
+
+1. Train a new Speech-to-Text model from scratch [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/coqui-ai/STT/blob/main/notebooks/train-your-first-coqui-STT-model.ipynb)
+2. Transfer learning (English --> Russian) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/coqui-ai/STT/blob/main/notebooks/easy-transfer-learning.ipynb)

notebooks/easy-transfer-learning.ipynb

@@ -0,0 +1,286 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "45ea3ef5",
+   "metadata": {},
+   "source": [
+    "# Easy transfer learning with 🐸 STT ⚡\n",
+    "\n",
+    "You want to train a Coqui (🐸) STT model, but you don't have a lot of data. What do you do?\n",
+    "\n",
+    "The answer 💡: Grab a pre-trained model and fine-tune it to your data. This is called `\"Transfer Learning\"` ⚡\n",
+    "\n",
+    "🐸 STT comes with transfer learning support out-of-the box.\n",
+    "\n",
+    "You can even take a pre-trained model and fine-tune it to _any new language_, even if the alphabets are completely different. Likewise, you can fine-tune a model to your own data and improve performance if the language is the same.\n",
+    "\n",
+    "In this notebook, we will:\n",
+    "\n",
+    "1. Download a pre-trained English STT model.\n",
+    "2. Download data for the Russian language.\n",
+    "3. Fine-tune the English model to Russian language.\n",
+    "4. Test the new Russian model and display its performance.\n",
+    "\n",
+    "So, let's jump right in!\n",
+    "\n",
+    "*PS - If you just want a working, off-the-shelf model, check out the [🐸 Model Zoo](https://www.coqui.ai/models)*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa2aec77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Install Coqui STT if you need to\n",
+    "# !git clone --depth 1 https://github.com/coqui-ai/STT.git\n",
+    "# !cd STT; pip install -U pip wheel setuptools; pip install ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c07a273",
+   "metadata": {},
+   "source": [
+    "## ✅ Download pre-trained English model\n",
+    "\n",
+    "We're going to download a very small (but very accurate) pre-trained STT model for English. This model was trained to only transcribe the English words \"yes\" and \"no\", but with transfer learning we can train a new model which could transcribe any words in any language. In this notebook, we will turn this \"constrained vocabulary\" English model into an \"open vocabulary\" Russian model.\n",
+    "\n",
+    "Coqui STT models as typically stored as checkpoints (for training) and protobufs (for deployment). For transfer learning, we want the **model checkpoints**.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "608d203f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Download pre-trained model\n",
+    "import os\n",
+    "import tarfile\n",
+    "from coqui_stt_training.util.downloader import maybe_download\n",
+    "\n",
+    "def download_pretrained_model():\n",
+    "    model_dir=\"english/\"\n",
+    "    if not os.path.exists(\"english/coqui-yesno-checkpoints\"):\n",
+    "        maybe_download(\"model.tar.gz\", model_dir, \"https://github.com/coqui-ai/STT-models/releases/download/english%2Fcoqui%2Fyesno-v0.0.1/coqui-yesno-checkpoints.tar.gz\")\n",
+    "        print('\\nNo extracted pre-trained model found. Extracting now...')\n",
+    "        tar = tarfile.open(\"english/model.tar.gz\")\n",
+    "        tar.extractall(\"english/\")\n",
+    "        tar.close()\n",
+    "    else:\n",
+    "        print('Found \"english/coqui-yesno-checkpoints\" - not extracting.')\n",
+    "\n",
+    "# Download + extract pre-trained English model\n",
+    "download_pretrained_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed9dd7ab",
+   "metadata": {},
+   "source": [
+    "## ✅ Download data for Russian\n",
+    "\n",
+    "**First things first**: we need some data.\n",
+    "\n",
+    "We're training a Speech-to-Text model, so we need some _speech_ and we need some _text_. Specificially, we want _transcribed speech_. Let's download a Russian audio file and its transcript, pre-formatted for 🐸 STT. \n",
+    "\n",
+    "**Second things second**: we want a Russian alphabet. The output layer of a typical* 🐸 STT model represents letters in the alphabet. Let's download a Russian alphabet from Coqui and use that.\n",
+    "\n",
+    "*_If you are working with languages with large character sets (e.g. Chinese), you can set `bytes_output_mode=True` instead of supplying an `alphabet.txt` file. In this case, the output layer of the STT model will correspond to individual UTF-8 bytes instead of individual characters._"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5105ea7",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "### Download sample data\n",
+    "from coqui_stt_training.util.downloader import maybe_download\n",
+    "\n",
+    "def download_sample_data():\n",
+    "    data_dir=\"russian/\"\n",
+    "    maybe_download(\"ru.wav\", data_dir, \"https://raw.githubusercontent.com/coqui-ai/STT/main/data/smoke_test/russian_sample_data/ru.wav\")\n",
+    "    maybe_download(\"ru.csv\", data_dir, \"https://raw.githubusercontent.com/coqui-ai/STT/main/data/smoke_test/russian_sample_data/ru.csv\")\n",
+    "    maybe_download(\"alphabet.txt\", data_dir, \"https://raw.githubusercontent.com/coqui-ai/STT/main/data/smoke_test/russian_sample_data/alphabet.ru\")\n",
+    "\n",
+    "# Download sample Russian data\n",
+    "download_sample_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b46b7227",
+   "metadata": {},
+   "source": [
+    "## ✅ Configure the training run\n",
+    "\n",
+    "Coqui STT comes with a long list of hyperparameters you can tweak. We've set default values, but you can use `initialize_globals_from_args()` to set your own. \n",
+    "\n",
+    "You must **always** configure the paths to your data, and you must **always** configure your alphabet. For transfer learning, it's good practice to define different `load_checkpoint_dir` and `save_checkpoint_dir` paths so that you keep your new model (Russian STT) separate from the old one (English STT). The parameter `drop_source_layers` allows you to remove layers from the original (aka \"source\") model, and re-initialize them from scratch. If you are fine-tuning to a new alphabet you will have to use _at least_ `drop_source_layers=1` to remove the output layer and add a new output layer which matches your new alphabet.\n",
+    "\n",
+    "We are fine-tuning a pre-existing model, so `n_hidden` should be the same as the original English model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cff3c5a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from coqui_stt_training.util.config import initialize_globals_from_args\n",
+    "\n",
+    "initialize_globals_from_args(\n",
+    "    n_hidden=64,\n",
+    "    load_checkpoint_dir=\"english/coqui-yesno-checkpoints\",\n",
+    "    save_checkpoint_dir=\"russian/checkpoints\",\n",
+    "    drop_source_layers=1,\n",
+    "    alphabet_config_path=\"russian/alphabet.txt\",\n",
+    "    train_files=[\"russian/ru.csv\"],\n",
+    "    dev_files=[\"russian/ru.csv\"],\n",
+    "    epochs=200,\n",
+    "    load_cudnn=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "419828c1",
+   "metadata": {},
+   "source": [
+    "### View all Config settings (*Optional*) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cac6ea3d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from coqui_stt_training.util.config import Config\n",
+    "\n",
+    "print(Config.to_json())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8e700d1",
+   "metadata": {},
+   "source": [
+    "## ✅ Train a new Russian model\n",
+    "\n",
+    "Let's kick off a training run 🚀🚀🚀 (using the configure you set above).\n",
+    "\n",
+    "This notebook should work on either a GPU or a CPU. However, in case you're running this on _multiple_ GPUs we want to only use one, because the sample dataset (one audio file) is too small to split across multiple GPUs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8aab2195",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from coqui_stt_training.train import train, early_training_checks\n",
+    "import tensorflow.compat.v1 as tfv1\n",
+    "\n",
+    "# use maximum one GPU\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
+    "\n",
+    "early_training_checks()\n",
+    "\n",
+    "tfv1.reset_default_graph()\n",
+    "train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c87ba61",
+   "metadata": {},
+   "source": [
+    "## ✅ Configure the testing run\n",
+    "\n",
+    "Let's add the path to our testing data and update `load_checkpoint_dir` to our new model checkpoints."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2be7beb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from coqui_stt_training.util.config import Config\n",
+    "\n",
+    "Config.test_files=[\"russian/ru.csv\"]\n",
+    "Config.load_checkpoint_dir=\"russian/checkpoints\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6a5c971",
+   "metadata": {},
+   "source": [
+    "## ✅ Test the new Russian model\n",
+    "\n",
+    "We made it! 🙌\n",
+    "\n",
+    "Let's kick off the testing run, which displays performance metrics.\n",
+    "\n",
+    "We're committing the cardinal sin of ML 😈 (aka - testing on our training data) so you don't want to deploy this model into production. In this notebook we're focusing on the workflow itself, so it's forgivable 😇\n",
+    "\n",
+    "You can see from the test output that our tiny model has overfit to the data, and basically memorized this one sentence.\n",
+    "\n",
+    "When you start training your own models, make sure your testing data doesn't include your training data 😅"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6222dc69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from coqui_stt_training.train import test\n",
+    "\n",
+    "tfv1.reset_default_graph()\n",
+    "test()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/train-your-first-coqui-STT-model.ipynb

@@ -25,6 +25,18 @@
     "*PS - If you just want a working, off-the-shelf model, check out the [🐸 Model Zoo](https://www.coqui.ai/models)*"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa2aec78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Install Coqui STT if you need to\n",
+    "# !git clone --depth 1 https://github.com/coqui-ai/STT.git\n",
+    "# !cd STT; pip install -U pip wheel setuptools; pip install ."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "be5fe49c",
@@ -46,7 +58,7 @@
     "\n",
     "**Second things second**: we want an alphabet. The output layer of a typical* 🐸 STT model represents letters in the alphabet, and you should specify this alphabet before training. Let's download an English alphabet from Coqui and use that.\n",
     "\n",
-    "_*If you are working with languages with large character sets (e.g. Chinese), you can set `bytes_output_mode=True` instead of supplying an `alphabet.txt` file. In this case, the output layer of the STT model will correspond to individual UTF-8 bytes instead of individual characters._"
+    "*_If you are working with languages with large character sets (e.g. Chinese), you can set `bytes_output_mode=True` instead of supplying an `alphabet.txt` file. In this case, the output layer of the STT model will correspond to individual UTF-8 bytes instead of individual characters._"
    ]
   },
   {

training/coqui_stt_training/util/config.py

@@ -74,32 +74,20 @@ class _SttConfig(Coqpit):
         if self.dropout_rate6 < 0:
             self.dropout_rate6 = self.dropout_rate
 
-        # Set default checkpoint dir
+        # Checkpoint dir logic #
-        # If separate save and load flags were not specified, default to load and save
+        if self.checkpoint_dir:
-        # from the same dir.
+            # checkpoint_dir always overrides {save,load}_checkpoint_dir
-
-        # if save_checkpoint_dir hasn't been set, or checkpoint_dir is new
-        if (not self.save_checkpoint_dir) or (
-            self.save_checkpoint_dir is not self.checkpoint_dir
-        ):
-            if not self.checkpoint_dir:
-                self.checkpoint_dir = xdg.save_data_path(
-                    os.path.join("stt", "checkpoints")
-                )
             self.save_checkpoint_dir = self.checkpoint_dir
-            else:
-                self.save_checkpoint_dir = self.checkpoint_dir
-        # if load_checkpoint_dir hasn't been set, or checkpoint_dir is new
-        if (not self.load_checkpoint_dir) or (
-            self.load_checkpoint_dir is not self.checkpoint_dir
-        ):
-            if not self.checkpoint_dir:
-                self.checkpoint_dir = xdg.load_data_path(
-                    os.path.join("stt", "checkpoints")
-                )
             self.load_checkpoint_dir = self.checkpoint_dir
         else:
-                self.load_checkpoint_dir = self.checkpoint_dir
+            if not self.save_checkpoint_dir:
+                self.save_checkpoint_dir = xdg.save_data_path(
+                    os.path.join("stt", "checkpoints")
+                )
+            if not self.load_checkpoint_dir:
+                self.load_checkpoint_dir = xdg.save_data_path(
+                    os.path.join("stt", "checkpoints")
+                )
 
         if self.load_train not in ["last", "best", "init", "auto"]:
             self.load_train = "auto"