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coqui-tts
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print model r value as loading it

This commit is contained in:
erogol 2020-10-07 13:34:21 +02:00
parent 48a40c4730
commit e1eab1ce4b
4 changed files with 415 additions and 335 deletions
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1
TTS/tts/utils/io.py
View File

@ -20,6 +20,7 @@ def load_checkpoint(model, checkpoint_path, amp=None, use_cuda=False):
# set model stepsize # set model stepsize
if hasattr(model.decoder, 'r'): if hasattr(model.decoder, 'r'):
model.decoder.set_r(state['r']) model.decoder.set_r(state['r'])
print(" > Model r: ", state['r'])
return model, state return model, state

644
notebooks/DDC_TTS_and_MultiBand_MelGAN_TF_Example.ipynb
View File

@ -1,316 +1,346 @@
{ {
"nbformat": 4, "cells": [
"nbformat_minor": 0, {
"metadata": { "cell_type": "markdown",
"colab": { "metadata": {
"name": "DDC-TTS_and_MultiBand-MelGAN_TF_Example.ipynb", "Collapsed": "false",
"provenance": [], "colab_type": "text",
"collapsed_sections": [], "id": "6LWsNd3_M3MP"
"toc_visible": true },
}, "source": [
"kernelspec": { "# Mozilla TTS on CPU Real-Time Speech Synthesis with Tensorflow"
"name": "python3", ]
"display_name": "Python 3"
}
}, },
"cells": [ {
{ "cell_type": "markdown",
"cell_type": "markdown", "metadata": {
"metadata": { "Collapsed": "false",
"id": "6LWsNd3_M3MP", "colab_type": "text",
"colab_type": "text" "id": "FAqrSIWgLyP0"
}, },
"source": [ "source": [
"# Mozilla TTS on CPU Real-Time Speech Synthesis with Tensorflow" "**These models are converted from released [PyTorch models](https://colab.research.google.com/drive/1u_16ZzHjKYFn1HNVuA4Qf_i2MMFB9olY?usp=sharing) using our TF utilities provided in Mozilla TTS.**\n",
] "\n",
"These TF models support TF 2.2 and for different versions you might need to\n",
"regenerate them. \n",
"\n",
"We use Tacotron2 and MultiBand-Melgan models and LJSpeech dataset.\n",
"\n",
"Tacotron2 is trained using [Double Decoder Consistency](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/) (DDC) only for 130K steps (3 days) with a single GPU.\n",
"\n",
"MultiBand-Melgan is trained 1.45M steps with real spectrograms.\n",
"\n",
"Note that both model performances can be improved with more training.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false",
"colab_type": "text",
"id": "Ku-dA4DKoeXk"
},
"source": [
"### Download Models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 162
}, },
{ "colab_type": "code",
"cell_type": "markdown", "id": "jGIgnWhGsxU1",
"metadata": { "outputId": "08b0dddd-4edf-48c9-e8e5-a419b36a5c3d",
"id": "FAqrSIWgLyP0", "tags": []
"colab_type": "text" },
}, "outputs": [],
"source": [ "source": [
"**These models are converted from released [PyTorch models](https://colab.research.google.com/drive/1u_16ZzHjKYFn1HNVuA4Qf_i2MMFB9olY?usp=sharing) using our TF utilities provided in Mozilla TTS.**\n", "!gdown --id 1p7OSEEW_Z7ORxNgfZwhMy7IiLE1s0aH7 -O data/tts_model.pkl\n",
"\n", "!gdown --id 18CQ6G6tBEOfvCHlPqP8EBI4xWbrr9dBc -O data/config.json"
"These TF models support TF 2.2 and for different versions you might need to\n", ]
"regenerate them. \n", },
"\n", {
"We use Tacotron2 and MultiBand-Melgan models and LJSpeech dataset.\n", "cell_type": "code",
"\n", "execution_count": null,
"Tacotron2 is trained using [Double Decoder Consistency](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/) (DDC) only for 130K steps (3 days) with a single GPU.\n", "metadata": {
"\n", "Collapsed": "false",
"MultiBand-Melgan is trained 1.45M steps with real spectrograms.\n", "colab": {
"\n", "base_uri": "https://localhost:8080/",
"Note that both model performances can be improved with more training.\n" "height": 235
]
}, },
{ "colab_type": "code",
"cell_type": "markdown", "id": "4dnpE0-kvTsu",
"metadata": { "outputId": "2fe836eb-c7e7-4f1e-9352-0142126bb19f",
"id": "Ku-dA4DKoeXk", "tags": []
"colab_type": "text" },
}, "outputs": [],
"source": [ "source": [
"### Download Models" "!gdown --id 1rHmj7CqD3Sfa716Y3ub_vpIBrQg_b1yF -O data/vocoder_model.pkl\n",
] "!gdown --id 1Rd0R_nRCrbjEdpOwq6XwZAktvugiBvmu -O data/config_vocoder.json\n",
"!gdown --id 11oY3Tv0kQtxK_JPgxrfesa99maVXHNxU -O data/scale_stats.npy"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false",
"colab_type": "text",
"id": "Zlgi8fPdpRF0"
},
"source": [
"### Define TTS function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false",
"colab": {},
"colab_type": "code",
"id": "f-Yc42nQZG5A"
},
"outputs": [],
"source": [
"def tts(model, text, CONFIG, p):\n",
" t_1 = time.time()\n",
" waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens, inputs = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, style_wav=None,\n",
" truncated=False, enable_eos_bos_chars=CONFIG.enable_eos_bos_chars,\n",
" backend='tf')\n",
" waveform = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0))\n",
" waveform = waveform.numpy()[0, 0]\n",
" rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n",
" tps = (time.time() - t_1) / len(waveform)\n",
" print(waveform.shape)\n",
" print(\" > Run-time: {}\".format(time.time() - t_1))\n",
" print(\" > Real-time factor: {}\".format(rtf))\n",
" print(\" > Time per step: {}\".format(tps))\n",
" IPython.display.display(IPython.display.Audio(waveform, rate=CONFIG.audio['sample_rate'])) \n",
" return alignment, mel_postnet_spec, stop_tokens, waveform"
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false",
"colab_type": "text",
"id": "ZksegYQepkFg"
},
"source": [
"### Load Models"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false",
"colab": {},
"colab_type": "code",
"id": "oVa0kOamprgj"
},
"outputs": [],
"source": [
"import os\n",
"import torch\n",
"import time\n",
"import IPython\n",
"\n",
"from TTS.tts.tf.utils.generic_utils import setup_model\n",
"from TTS.tts.tf.utils.io import load_checkpoint\n",
"from TTS.utils.io import load_config\n",
"from TTS.tts.utils.text.symbols import symbols, phonemes\n",
"from TTS.utils.audio import AudioProcessor\n",
"from TTS.tts.utils.synthesis import synthesis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false",
"colab": {},
"colab_type": "code",
"id": "EY-sHVO8IFSH"
},
"outputs": [],
"source": [
"# runtime settings\n",
"use_cuda = False"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false",
"colab": {},
"colab_type": "code",
"id": "_1aIUp2FpxOQ"
},
"outputs": [],
"source": [
"# model paths\n",
"TTS_MODEL = \"data/tts_model.pkl\"\n",
"TTS_CONFIG = \"data/config.json\"\n",
"VOCODER_MODEL = \"data/vocoder_model.pkl\"\n",
"VOCODER_CONFIG = \"data/config_vocoder.json\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false",
"colab": {},
"colab_type": "code",
"id": "CpgmdBVQplbv"
},
"outputs": [],
"source": [
"# load configs\n",
"TTS_CONFIG = load_config(TTS_CONFIG)\n",
"VOCODER_CONFIG = load_config(VOCODER_CONFIG)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 471
}, },
{ "colab_type": "code",
"cell_type": "code", "id": "zmrQxiozIUVE",
"metadata": { "outputId": "fa71bd05-401f-4e5b-a6f7-60ae765966db",
"id": "jGIgnWhGsxU1", "tags": []
"colab_type": "code", },
"colab": { "outputs": [],
"base_uri": "https://localhost:8080/", "source": [
"height": 162 "# load the audio processor\n",
}, "TTS_CONFIG.audio['stats_path'] = 'data/scale_stats.npy'\n",
"outputId": "08b0dddd-4edf-48c9-e8e5-a419b36a5c3d", "ap = AudioProcessor(**TTS_CONFIG.audio) "
"tags": [] ]
}, },
"source": [ {
"!gdown --id 1p7OSEEW_Z7ORxNgfZwhMy7IiLE1s0aH7 -O data/tts_model.pkl\n", "cell_type": "code",
"!gdown --id 18CQ6G6tBEOfvCHlPqP8EBI4xWbrr9dBc -O data/config.json" "execution_count": null,
], "metadata": {
"execution_count": null, "Collapsed": "false",
"outputs": [] "colab": {
"base_uri": "https://localhost:8080/",
"height": 72
}, },
{ "colab_type": "code",
"cell_type": "code", "id": "8fLoI4ipqMeS",
"metadata": { "outputId": "595d990f-930d-4698-ee14-77796b5eed7d",
"id": "4dnpE0-kvTsu", "tags": []
"colab_type": "code", },
"colab": { "outputs": [],
"base_uri": "https://localhost:8080/", "source": [
"height": 235 "# LOAD TTS MODEL\n",
}, "# multi speaker \n",
"outputId": "2fe836eb-c7e7-4f1e-9352-0142126bb19f", "speaker_id = None\n",
"tags": [] "speakers = []\n",
}, "\n",
"source": [ "# load the model\n",
"!gdown --id 1rHmj7CqD3Sfa716Y3ub_vpIBrQg_b1yF -O data/vocoder_model.pkl\n", "num_chars = len(phonemes) if TTS_CONFIG.use_phonemes else len(symbols)\n",
"!gdown --id 1Rd0R_nRCrbjEdpOwq6XwZAktvugiBvmu -O data/config_vocoder.json\n", "model = setup_model(num_chars, len(speakers), TTS_CONFIG)\n",
"!gdown --id 11oY3Tv0kQtxK_JPgxrfesa99maVXHNxU -O data/scale_stats.npy" "model.build_inference()\n",
], "model = load_checkpoint(model, TTS_MODEL)\n",
"execution_count": null, "model.decoder.set_max_decoder_steps(1000)"
"outputs": [] ]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 489
}, },
{ "colab_type": "code",
"cell_type": "markdown", "id": "zKoq0GgzqzhQ",
"metadata": { "outputId": "2cc3deae-144f-4465-da3b-98628d948506"
"id": "Zlgi8fPdpRF0", },
"colab_type": "text" "outputs": [],
}, "source": [
"source": [ "from TTS.vocoder.tf.utils.generic_utils import setup_generator\n",
"### Define TTS function" "from TTS.vocoder.tf.utils.io import load_checkpoint\n",
] "\n",
"# LOAD VOCODER MODEL\n",
"vocoder_model = setup_generator(VOCODER_CONFIG)\n",
"vocoder_model.build_inference()\n",
"vocoder_model = load_checkpoint(vocoder_model, VOCODER_MODEL)\n",
"vocoder_model.inference_padding = 0\n",
"\n",
"ap_vocoder = AudioProcessor(**VOCODER_CONFIG['audio']) "
]
},
{
"cell_type": "markdown",
"metadata": {
"Collapsed": "false",
"colab_type": "text",
"id": "Ws_YkPKsLgo-"
},
"source": [
"## Run Inference"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"Collapsed": "false",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 134
}, },
{ "colab_type": "code",
"cell_type": "code", "id": "FuWxZ9Ey5Puj",
"metadata": { "outputId": "07ede6e5-06e6-4612-f687-7984d20e5254"
"id": "f-Yc42nQZG5A", },
"colab_type": "code", "outputs": [],
"colab": {} "source": [
}, "sentence = \"Bill got in the habit of asking himself “Is that thought true?” and if he wasn’t absolutely certain it was, he just let it go.\"\n",
"source": [ "align, spec, stop_tokens, wav = tts(model, sentence, TTS_CONFIG, ap)"
"def tts(model, text, CONFIG, p):\n", ]
" t_1 = time.time()\n", }
" waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens, inputs = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, style_wav=None,\n", ],
" truncated=False, enable_eos_bos_chars=CONFIG.enable_eos_bos_chars,\n", "metadata": {
" backend='tf')\n", "colab": {
" waveform = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0))\n", "collapsed_sections": [],
" waveform = waveform.numpy()[0, 0]\n", "name": "DDC-TTS_and_MultiBand-MelGAN_TF_Example.ipynb",
" rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n", "provenance": [],
" tps = (time.time() - t_1) / len(waveform)\n", "toc_visible": true
" print(waveform.shape)\n", },
" print(\" > Run-time: {}\".format(time.time() - t_1))\n", "kernelspec": {
" print(\" > Real-time factor: {}\".format(rtf))\n", "display_name": "Python 3",
" print(\" > Time per step: {}\".format(tps))\n", "language": "python",
" IPython.display.display(IPython.display.Audio(waveform, rate=CONFIG.audio['sample_rate'])) \n", "name": "python3"
" return alignment, mel_postnet_spec, stop_tokens, waveform" },
], "language_info": {
"execution_count": null, "codemirror_mode": {
"outputs": [] "name": "ipython",
}, "version": 3
{ },
"cell_type": "markdown", "file_extension": ".py",
"metadata": { "mimetype": "text/x-python",
"id": "ZksegYQepkFg", "name": "python",
"colab_type": "text" "nbconvert_exporter": "python",
}, "pygments_lexer": "ipython3",
"source": [ "version": "3.7.7"
"### Load Models" }
] },
}, "nbformat": 4,
{ "nbformat_minor": 4
"cell_type": "code",
"metadata": {
"id": "oVa0kOamprgj",
"colab_type": "code",
"colab": {}
},
"source": [
"import os\n",
"import torch\n",
"import time\n",
"import IPython\n",
"\n",
"from TTS.tts.tf.utils.generic_utils import setup_model\n",
"from TTS.tts.tf.utils.io import load_checkpoint\n",
"from TTS.utils.io import load_config\n",
"from TTS.tts.utils.text.symbols import symbols, phonemes\n",
"from TTS.utils.audio import AudioProcessor\n",
"from TTS.tts.utils.synthesis import synthesis"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "EY-sHVO8IFSH",
"colab_type": "code",
"colab": {}
},
"source": [
"# runtime settings\n",
"use_cuda = False"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "_1aIUp2FpxOQ",
"colab_type": "code",
"colab": {}
},
"source": [
"# model paths\n",
"TTS_MODEL = \"data/tts_model.pkl\"\n",
"TTS_CONFIG = \"data/config.json\"\n",
"VOCODER_MODEL = \"data/vocoder_model.pkl\"\n",
"VOCODER_CONFIG = \"data/config_vocoder.json\""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "CpgmdBVQplbv",
"colab_type": "code",
"colab": {}
},
"source": [
"# load configs\n",
"TTS_CONFIG = load_config(TTS_CONFIG)\n",
"VOCODER_CONFIG = load_config(VOCODER_CONFIG)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zmrQxiozIUVE",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 471
},
"outputId": "fa71bd05-401f-4e5b-a6f7-60ae765966db",
"tags": []
},
"source": [
"# load the audio processor\n",
"TTS_CONFIG.audio['stats_path'] = 'data/scale_stats.npy'\n",
"ap = AudioProcessor(**TTS_CONFIG.audio) "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8fLoI4ipqMeS",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 72
},
"outputId": "595d990f-930d-4698-ee14-77796b5eed7d",
"tags": []
},
"source": [
"# LOAD TTS MODEL\n",
"# multi speaker \n",
"speaker_id = None\n",
"speakers = []\n",
"\n",
"# load the model\n",
"num_chars = len(phonemes) if TTS_CONFIG.use_phonemes else len(symbols)\n",
"model = setup_model(num_chars, len(speakers), TTS_CONFIG)\n",
"model.build_inference()\n",
"model = load_checkpoint(model, TTS_MODEL)\n",
"model.decoder.set_max_decoder_steps(1000)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zKoq0GgzqzhQ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 489
},
"outputId": "2cc3deae-144f-4465-da3b-98628d948506"
},
"source": [
"from TTS.vocoder.tf.utils.generic_utils import setup_generator\n",
"from TTS.vocoder.tf.utils.io import load_checkpoint\n",
"\n",
"# LOAD VOCODER MODEL\n",
"vocoder_model = setup_generator(VOCODER_CONFIG)\n",
"vocoder_model.build_inference()\n",
"vocoder_model = load_checkpoint(vocoder_model, VOCODER_MODEL)\n",
"vocoder_model.inference_padding = 0\n",
"\n",
"ap_vocoder = AudioProcessor(**VOCODER_CONFIG['audio']) "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ws_YkPKsLgo-",
"colab_type": "text"
},
"source": [
"## Run Inference"
]
},
{
"cell_type": "code",
"metadata": {
"id": "FuWxZ9Ey5Puj",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 134
},
"outputId": "07ede6e5-06e6-4612-f687-7984d20e5254"
},
"source": [
"sentence = \"Bill got in the habit of asking himself “Is that thought true?” and if he wasn’t absolutely certain it was, he just let it go.\"\n",
"align, spec, stop_tokens, wav = tts(model, sentence, TTS_CONFIG, ap)"
],
"execution_count": null,
"outputs": []
}
]
} }

2
notebooks/TestAttention.ipynb
View File

@ -181,7 +181,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.4" "version": "3.7.7"
} }
}, },
"nbformat": 4, "nbformat": 4,

101
notebooks/dataset_analysis/AnalyzeDataset.ipynb
View File

@ -3,7 +3,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"TTS_PATH = \"/home/erogol/projects/\"" "TTS_PATH = \"/home/erogol/projects/\""
@ -12,7 +14,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"import os\n", "import os\n",
@ -34,7 +38,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"DATA_PATH = \"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/\"\n", "DATA_PATH = \"/home/erogol/Data/m-ai-labs/de_DE/by_book/male/karlsson/\"\n",
@ -58,7 +64,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# use your own preprocessor at this stage - TTS/datasets/proprocess.py\n", "# use your own preprocessor at this stage - TTS/datasets/proprocess.py\n",
@ -69,7 +77,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# check wavs if exist\n", "# check wavs if exist\n",
@ -84,7 +94,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# show duplicate items\n", "# show duplicate items\n",
@ -95,7 +107,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"def load_item(item):\n", "def load_item(item):\n",
@ -121,7 +135,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# count words in the dataset\n", "# count words in the dataset\n",
@ -136,7 +152,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"text_vs_durs = {} # text length vs audio duration\n", "text_vs_durs = {} # text length vs audio duration\n",
@ -155,7 +173,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# text_len vs avg_audio_len, median_audio_len, std_audio_len\n", "# text_len vs avg_audio_len, median_audio_len, std_audio_len\n",
@ -170,7 +190,9 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"source": [ "source": [
"### Avg audio length per char" "### Avg audio length per char"
] ]
@ -178,7 +200,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"for item in data:\n", "for item in data:\n",
@ -189,7 +213,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sec_per_chars = []\n", "sec_per_chars = []\n",
@ -205,7 +231,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"mean = np.mean(sec_per_chars)\n", "mean = np.mean(sec_per_chars)\n",
@ -217,7 +245,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"dist = norm(mean, std)\n", "dist = norm(mean, std)\n",
@ -234,7 +264,9 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"source": [ "source": [
"### Plot Dataset Statistics" "### Plot Dataset Statistics"
] ]
@ -242,7 +274,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"plt.title(\"text length vs mean audio duration\")\n", "plt.title(\"text length vs mean audio duration\")\n",
@ -252,7 +286,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"plt.title(\"text length vs median audio duration\")\n", "plt.title(\"text length vs median audio duration\")\n",
@ -262,7 +298,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"plt.title(\"text length vs STD\")\n", "plt.title(\"text length vs STD\")\n",
@ -272,7 +310,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"plt.title(\"text length vs # instances\")\n", "plt.title(\"text length vs # instances\")\n",
@ -281,7 +321,9 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"source": [ "source": [
"### Check words frequencies" "### Check words frequencies"
] ]
@ -289,7 +331,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"w_count_df = pd.DataFrame.from_dict(w_count, orient='index')\n", "w_count_df = pd.DataFrame.from_dict(w_count, orient='index')\n",
@ -300,6 +344,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {
"Collapsed": "false",
"scrolled": true "scrolled": true
}, },
"outputs": [], "outputs": [],
@ -310,7 +355,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# check a certain word\n", "# check a certain word\n",
@ -320,7 +367,9 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {
"Collapsed": "false"
},
"outputs": [], "outputs": [],
"source": [ "source": [
"# fequency bar plot - it takes time!!\n", "# fequency bar plot - it takes time!!\n",
@ -344,9 +393,9 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.2" "version": "3.7.7"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 2 "nbformat_minor": 4
} }