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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/github/sayakpaul/TensorFlowTTS/blob/master/notebooks/TensorFlowTTS_FastSpeech_with_TFLite.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sSxhJVHSDGGc"
},
"source": [
"##### Copyright 2020 The TensorFlow Authors. All Rights Reserved.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Q0ySjtvCD6nJ"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Do75nZO17R_g"
},
"source": [
"Authors : [jaeyoo@](https://github.com/jaeyoo), [khanhlvg@](https://github.com/khanhlvg), [abattery@](https://github.com/abattery), [thaink@](https://github.com/thaink) (Google Research) (refactored by [sayakpaul](https://github.com/sayakpaul) (PyImageSearch))\n",
"\n",
"Created : 2020-07-03 KST\n",
"\n",
"Last updated : 2020-07-04 KST\n",
"\n",
"-----\n",
"Change logs\n",
"* 2020-07-04 KST : Update notebook with the latest repo.\n",
" * https://github.com/TensorSpeech/TensorflowTTS/pull/84 merged.\n",
"* 2020-07-03 KST : First implementation (outputs : `fastspeech_quant.tflite`)\n",
" * varied-length input tensor, varied-length output tensor\n",
" * Inference on tflite works well.\n",
"* 2020-12-22 IST: Notebook runs end-to-end on Colab.\n",
"-----\n",
"\n",
"**Status** : successfully converted (`fastspeech_quant.tflite`)\n",
"\n",
"**Disclaimer** \n",
"- This colab doesn't care about the latency, so it compressed the model with quantization. (112 MB -> 28 MB)\n",
"- The TFLite file doesn't have LJSpeechProcessor. So you need to run it before feeding input vectors.\n",
"- `tf-nightly>=2.4.0-dev20200630`\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p5aF0cRBv57s"
},
"source": [
"# Generate voice with FastSpeech"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VJTsCmhciNfz"
},
"outputs": [],
"source": [
"!git clone https://github.com/TensorSpeech/TensorFlowTTS.git\n",
"!cd TensorFlowTTS\n",
"!pip install /content/TensorFlowTTS/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-vKfQMu7PiVL"
},
"outputs": [],
"source": [
"!pip install -q tf-nightly"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vFZ9aWOnP3y_"
},
"source": [
"**Another runtime restart is required.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EHHcYEUyon5W",
"outputId": "f89c5c36-a33a-48c2-9fb6-11ced05d4eea"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import yaml\n",
"import tensorflow as tf\n",
"\n",
"import sys\n",
"sys.path.append('/content/TensorFlowTTS')\n",
"\n",
"from tensorflow_tts.inference import AutoProcessor\n",
"from tensorflow_tts.inference import AutoConfig\n",
"from tensorflow_tts.inference import TFAutoModel\n",
"\n",
"from IPython.display import Audio\n",
"print(tf.__version__) # check if >= 2.4.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "nBr1A7MBSm6u"
},
"outputs": [],
"source": [
"# initialize melgan model\n",
"melgan = TFAutoModel.from_pretrained(\"tensorspeech/tts-melgan-ljspeech-en\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VG0PwedpqFhd"
},
"outputs": [],
"source": [
"# initialize FastSpeech model.\n",
"fastspeech = TFAutoModel.from_pretraned(\"tensorspeech/tts-fastspeech-ljspeech-en\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7cPCQoxam3vp",
"outputId": "3dcede9a-045c-4599-dea8-0a6c1e38d942"
},
"outputs": [],
"source": [
"input_text = \"Recent research at Harvard has shown meditating\\\n",
"for as little as 8 weeks, can actually increase the grey matter in the \\\n",
"parts of the brain responsible for emotional regulation, and learning.\"\n",
"\n",
"processor = AutoProcessor.from_pretrained(\"tensorspeech/tts-fastspeech-ljspeech-en\")\n",
"input_ids = processor.text_to_sequence(input_text.lower())\n",
"\n",
"mel_before, mel_after, duration_outputs, _, _ = fastspeech.inference(\n",
" input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),\n",
" speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),\n",
" speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),\n",
" f0_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),\n",
" energy_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),\n",
")\n",
"\n",
"audio_before = melgan(mel_before)[0, :, 0]\n",
"audio_after = melgan(mel_after)[0, :, 0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
},
"id": "SxVxtZI5sDF-",
"outputId": "b9bbeff9-2b25-4ef2-9979-e6f41859beb3"
},
"outputs": [],
"source": [
"Audio(data=audio_before, rate=22050)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
},
"id": "i5gV4y9RpLBA",
"outputId": "fdcbb41f-ad7b-4320-86f2-452385adda1e"
},
"outputs": [],
"source": [
"Audio(data=audio_after, rate=22050)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "38xzKgqgwbLl"
},
"source": [
"# Convert to TFLite"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "j3eBgJr1CfqF"
},
"outputs": [],
"source": [
"# Concrete Function\n",
"fastspeech_concrete_function = fastspeech.inference_tflite.get_concrete_function()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "d9CUR0UD8O9w"
},
"outputs": [],
"source": [
"converter = tf.lite.TFLiteConverter.from_concrete_functions(\n",
" [fastspeech_concrete_function]\n",
")\n",
"converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
"converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,\n",
" tf.lite.OpsSet.SELECT_TF_OPS]\n",
"tflite_model = converter.convert()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IjLkV0wlIVq1",
"outputId": "bc6fc0aa-31ab-4188-a851-6135abd8a5cd"
},
"outputs": [],
"source": [
"# Save the TF Lite model.\n",
"with open('fastspeech_quant.tflite', 'wb') as f:\n",
" f.write(tflite_model)\n",
"\n",
"print('Model size is %f MBs.' % (len(tflite_model) / 1024 / 1024.0) )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "gLoUH69hJkIK"
},
"outputs": [],
"source": [
"## Download the TF Lite model\n",
"#from google.colab import files\n",
"#files.download('fastspeech_quant.tflite') "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1WqL_NEbtL5K"
},
"source": [
"# Inference from TFLite"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JjNnqWlItLXi"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"\n",
"# Load the TFLite model and allocate tensors.\n",
"interpreter = tf.lite.Interpreter(model_path='fastspeech_quant.tflite')\n",
"\n",
"# Get input and output tensors.\n",
"input_details = interpreter.get_input_details()\n",
"output_details = interpreter.get_output_details()\n",
"\n",
"# Prepare input data.\n",
"def prepare_input(input_ids):\n",
" input_ids = tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0)\n",
" return (input_ids,\n",
" tf.convert_to_tensor([0], tf.int32),\n",
" tf.convert_to_tensor([1.0], dtype=tf.float32),\n",
" tf.convert_to_tensor([1.0], dtype=tf.float32),\n",
" tf.convert_to_tensor([1.0], dtype=tf.float32))\n",
"\n",
"# Test the model on random input data.\n",
"def infer(input_text):\n",
" processor = AutoProcessor.from_pretrained(pretrained_path=\"ljspeech_mapper.json\")\n",
" input_ids = processor.text_to_sequence(input_text.lower())\n",
" interpreter.resize_tensor_input(input_details[0]['index'], \n",
" [1, len(input_ids)])\n",
" interpreter.resize_tensor_input(input_details[1]['index'], \n",
" [1])\n",
" interpreter.resize_tensor_input(input_details[2]['index'], \n",
" [1])\n",
" interpreter.resize_tensor_input(input_details[3]['index'], \n",
" [1])\n",
" interpreter.resize_tensor_input(input_details[4]['index'], \n",
" [1])\n",
" interpreter.allocate_tensors()\n",
" input_data = prepare_input(input_ids)\n",
" for i, detail in enumerate(input_details):\n",
" input_shape = detail['shape_signature']\n",
" interpreter.set_tensor(detail['index'], input_data[i])\n",
"\n",
" interpreter.invoke()\n",
"\n",
" # The function `get_tensor()` returns a copy of the tensor data.\n",
" # Use `tensor()` in order to get a pointer to the tensor.\n",
" return (interpreter.get_tensor(output_details[0]['index']),\n",
" interpreter.get_tensor(output_details[1]['index']))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dRgCO2UfdrBe"
},
"outputs": [],
"source": [
"input_text = \"Recent research at Harvard has shown meditating\\\n",
"for as little as 8 weeks, can actually increase the grey matter in the \\\n",
"parts of the brain responsible for emotional regulation, and learning.\"\n",
"\n",
"decoder_output_tflite, mel_output_tflite = infer(input_text)\n",
"audio_before_tflite = melgan(decoder_output_tflite)[0, :, 0]\n",
"audio_after_tflite = melgan(mel_output_tflite)[0, :, 0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
},
"id": "vajrYnWAX31f",
"outputId": "6ec5fcf3-c66b-4dac-f749-e7149beb81eb"
},
"outputs": [],
"source": [
"Audio(data=audio_before_tflite, rate=22050)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
},
"id": "-eJ5QGc5X_Tc",
"outputId": "85654556-89ee-4b3e-9100-f60390af26f2",
"scrolled": true
},
"outputs": [],
"source": [
"Audio(data=audio_after_tflite, rate=22050)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "iht1FDZUd0Ig"
},
"outputs": [],
"source": [
"input_text = \"I love TensorFlow Lite converted FastSpeech with quantization. \\\n",
"The converted model file is of 28.6 Mega bytes.\"\n",
"\n",
"decoder_output_tflite, mel_output_tflite = infer(input_text)\n",
"audio_before_tflite = melgan(decoder_output_tflite)[0, :, 0]\n",
"audio_after_tflite = melgan(mel_output_tflite)[0, :, 0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
},
"id": "ZJVtr-D3d6rr",
"outputId": "138a0cc3-d367-4a0c-872a-47ca25e12f69"
},
"outputs": [],
"source": [
"Audio(data=audio_before_tflite, rate=22050)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
},
"id": "mBU2Zdl1d8ZI",
"outputId": "ce188fbf-5d86-474f-84ee-b22baac6f417"
},
"outputs": [],
"source": [
"Audio(data=audio_after_tflite, rate=22050)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"include_colab_link": true,
"name": "TensorFlowTTS - FastSpeech with TFLite",
"provenance": []
},
"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": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
|