mirror of https://github.com/coqui-ai/TTS.git
428 lines
9.8 KiB
Plaintext
428 lines
9.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"# TTS_PATH = \"/home/erogol/projects/\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import sys\n",
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"import librosa\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from scipy.stats import norm\n",
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"from tqdm import tqdm_notebook as tqdm\n",
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"from multiprocessing import Pool\n",
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"from matplotlib import pylab as plt\n",
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"from collections import Counter\n",
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"from TTS.config.shared_configs import BaseDatasetConfig\n",
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"from TTS.tts.datasets import load_tts_samples\n",
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"from TTS.tts.datasets.formatters import *\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"NUM_PROC = 8\n",
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"DATASET_CONFIG = BaseDatasetConfig(\n",
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" formatter=\"ljspeech\", meta_file_train=\"metadata.csv\", path=\"/absolute/path/to/your/dataset/\"\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def formatter(root_path, meta_file, **kwargs): # pylint: disable=unused-argument\n",
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" txt_file = os.path.join(root_path, meta_file)\n",
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" items = []\n",
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" speaker_name = \"myspeaker\"\n",
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" with open(txt_file, \"r\", encoding=\"utf-8\") as ttf:\n",
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" for line in ttf:\n",
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" cols = line.split(\"|\")\n",
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" wav_file = os.path.join(root_path, \"wavs\", cols[0] + \".wav\") \n",
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" text = cols[1]\n",
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" items.append({\"text\": text, \"audio_file\": wav_file, \"speaker_name\": speaker_name, \"root_path\": root_path})\n",
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" return items"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"# use your own preprocessor at this stage - TTS/datasets/proprocess.py\n",
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"train_samples, eval_samples = load_tts_samples(DATASET_CONFIG, eval_split=True, formatter=formatter)\n",
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"if eval_samples is not None:\n",
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" items = train_samples + eval_samples\n",
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"else:\n",
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" items = train_samples\n",
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"print(\" > Number of audio files: {}\".format(len(items)))\n",
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"print(items[1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"# check wavs if exist\n",
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"wav_files = []\n",
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"for item in items:\n",
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" wav_file = item[\"audio_file\"].strip()\n",
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" wav_files.append(wav_file)\n",
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" if not os.path.exists(wav_file):\n",
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" print(wav_file)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"# show duplicate items\n",
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"c = Counter(wav_files)\n",
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"print([item for item, count in c.items() if count > 1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"item"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"def load_item(item):\n",
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" text = item[\"text\"].strip()\n",
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" file_name = item[\"audio_file\"].strip()\n",
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" audio, sr = librosa.load(file_name, sr=None)\n",
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" audio_len = len(audio) / sr\n",
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" text_len = len(text)\n",
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" return file_name, text, text_len, audio, audio_len\n",
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"\n",
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"# This will take a while depending on size of dataset\n",
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"if NUM_PROC == 1:\n",
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" data = []\n",
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" for m in tqdm(items):\n",
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" data += [load_item(m)]\n",
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"else:\n",
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" with Pool(8) as p:\n",
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" data = list(tqdm(p.imap(load_item, items), total=len(items)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"# count words in the dataset\n",
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"w_count = Counter()\n",
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"for item in tqdm(data):\n",
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" text = item[1].lower().strip()\n",
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" for word in text.split():\n",
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" w_count[word] += 1\n",
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"print(\" > Number of words: {}\".format(len(w_count)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"text_vs_durs = {} # text length vs audio duration\n",
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"text_len_counter = Counter() # number of sentences with the keyed length\n",
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"for item in tqdm(data):\n",
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" text = item[1].lower().strip()\n",
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" text_len = len(text)\n",
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" text_len_counter[text_len] += 1\n",
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" audio_len = item[-1]\n",
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" try:\n",
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" text_vs_durs[text_len] += [audio_len]\n",
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" except:\n",
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" text_vs_durs[text_len] = [audio_len]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"# text_len vs avg_audio_len, median_audio_len, std_audio_len\n",
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"text_vs_avg = {}\n",
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"text_vs_median = {}\n",
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"text_vs_std = {}\n",
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"for key, durs in text_vs_durs.items():\n",
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" text_vs_avg[key] = np.mean(durs)\n",
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" text_vs_median[key] = np.median(durs)\n",
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" text_vs_std[key] = np.std(durs)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"Collapsed": "false"
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},
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"source": [
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"### Avg audio length per char"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"for item in data:\n",
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" if item[-1] < 2:\n",
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" print(item)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"sec_per_chars = []\n",
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"for item in data:\n",
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" text = item[1]\n",
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" dur = item[-1]\n",
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" sec_per_char = dur / len(text)\n",
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" sec_per_chars.append(sec_per_char)\n",
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"# sec_per_char /= len(data)\n",
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"# print(sec_per_char)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"mean = np.mean(sec_per_chars)\n",
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"std = np.std(sec_per_chars)\n",
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"print(mean)\n",
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"print(std)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"dist = norm(mean, std)\n",
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"\n",
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"# find irregular instances long or short voice durations\n",
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"for item in data:\n",
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" text = item[1]\n",
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" dur = item[-1]\n",
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" sec_per_char = dur / len(text)\n",
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" pdf =norm.pdf(sec_per_char)\n",
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" if pdf < 0.39:\n",
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" print(item)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"Collapsed": "false"
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},
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"source": [
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"### Plot Dataset Statistics"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"plt.title(\"text length vs mean audio duration\")\n",
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"plt.scatter(list(text_vs_avg.keys()), list(text_vs_avg.values()))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"plt.title(\"text length vs median audio duration\")\n",
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"plt.scatter(list(text_vs_median.keys()), list(text_vs_median.values()))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"plt.title(\"text length vs STD\")\n",
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"plt.scatter(list(text_vs_std.keys()), list(text_vs_std.values()))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"plt.title(\"text length vs # instances\")\n",
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"plt.scatter(list(text_len_counter.keys()), list(text_len_counter.values()))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"Collapsed": "false"
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},
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"source": [
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"### Check words frequencies"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"w_count_df = pd.DataFrame.from_dict(w_count, orient='index')\n",
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"w_count_df.sort_values(0, ascending=False, inplace=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false",
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"w_count_df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"# check a certain word\n",
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"w_count_df.at['minute', 0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"Collapsed": "false"
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},
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"outputs": [],
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"source": [
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"# fequency bar plot - it takes time!!\n",
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"w_count_df.plot.bar()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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