Jupyter Notbook for phoneme coverage analysis

This jupyter notebook checks dataset configured in config.json for phoneme coverage. As mentioned here https://github.com/mozilla/TTS/wiki/Dataset#what-makes-a-good-dataset a good phoneme coverage is recommended.

Most parameters will be taken from config.json file in mozilla tts repo so please ensure it's configured correctly for your dataset. This notebook used lots of existring code from the TTS repo to ensure future compatibility.

Many thanks to Neil Stoker supporting me on this topic :-).

I provide this notebook without any warrenty but it's hopefully useful for your dataset analysis.

Happy TTS'ing :-)

Thorsten Müller

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# set some vars
# TTS_PATH = "/home/thorsten/___dev/tts/mozilla/TTS"
CONFIG_FILE = "/path/to/config/config.json"
CHARS_TO_REMOVE = ".,:!?'"

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# import stuff
from TTS.config import load_config
from TTS.tts.datasets import load_tts_samples
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from tqdm import tqdm
from matplotlib import pylab as plt
from multiprocessing import Pool, cpu_count

# extra imports that might not be included in requirements.txt
import collections
import operator

%matplotlib inline

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# Load config.json properties
CONFIG = load_config(CONFIG_FILE)

# Load some properties from config.json
CONFIG_METADATA = load_tts_samples(CONFIG.datasets)[0]
CONFIG_METADATA = CONFIG_METADATA
CONFIG_DATASET = CONFIG.datasets[0]
CONFIG_PHONEME_LANGUAGE = CONFIG.phoneme_language
CONFIG_TEXT_CLEANER = CONFIG.text_cleaner
CONFIG_ENABLE_EOS_BOS_CHARS = CONFIG.enable_eos_bos_chars

# Will be printed on generated output graph
CONFIG_RUN_NAME = CONFIG.run_name
CONFIG_RUN_DESC = CONFIG.run_description

# Needed to convert text to phonemes and phonemes to ids
tokenizer, config = TTSTokenizer.init_from_config(CONFIG)

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# print some debug information on loaded config values
print(" > Run name: " + CONFIG_RUN_NAME + " (" + CONFIG_RUN_DESC + ")")
print(" > Dataset files: " + str(len(CONFIG_METADATA)))
print(" > Phoneme language: " + CONFIG_PHONEME_LANGUAGE)
print(" > Used text cleaner: " + CONFIG_TEXT_CLEANER)
print(" > Enable eos bos chars: " + str(CONFIG_ENABLE_EOS_BOS_CHARS))

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def get_phoneme_from_sequence(text):
    temp_list = []
    if len(text["text"]) > 0:
        #temp_text = text[0].rstrip('\n')
        temp_text = text["text"].rstrip('\n')
        for rm_bad_chars in CHARS_TO_REMOVE:
            temp_text = temp_text.replace(rm_bad_chars,"")
        seq = tokenizer.text_to_ids(temp_text)
        text = tokenizer.ids_to_text(seq)
        text = text.replace(" ","")
        temp_list.append(text)
    return temp_list

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# Get phonemes from metadata
phonemes = []

with Pool(cpu_count()-1) as p:
    
    phonemes = list(tqdm(p.imap(get_phoneme_from_sequence, CONFIG_METADATA), total=len(CONFIG_METADATA)))
    phonemes = [i for sub in phonemes for i in sub]

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s = ""
phonemeString = s.join(phonemes)

d = {}
collections._count_elements(d, phonemeString)
sorted_d = dict(sorted(d.items(), key=operator.itemgetter(1),reverse=True))

# remove useless keys
sorted_d.pop(' ', None)
sorted_d.pop('ˈ', None)

phonemesSum = len(phonemeString.replace(" ",""))

print("Dataset contains " + str(len(sorted_d)) + " different ipa phonemes.")
print("Dataset consists of " + str(phonemesSum) + " phonemes")

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print("5 rarest phonemes")

rareList = dict(sorted(sorted_d.items(), key=operator.itemgetter(1), reverse=False)[:5])
for key, value in rareList.items():
    print(key + " --> " + str(value) + " occurrences")

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# create plot from analysis result

x = []
y = []

for key, value in sorted_d.items():
    x.append(key)
    y.append(value)

plt.figure(figsize=(50,50))
plt.title("Phoneme coverage for " + CONFIG_RUN_NAME + " (" + CONFIG_RUN_DESC + ")", fontsize=50)
plt.xticks(fontsize=50)
plt.yticks(fontsize=50)
plt.barh(x,y, align='center', alpha=1.0)
plt.gca().invert_yaxis()
plt.ylabel('phoneme', fontsize=50)
plt.xlabel('occurrences', fontsize=50)

for i, v in enumerate(y):
    plt.text(v + 2, i - .2, str(v), fontsize=20)
    plt.text(v + 2, i + .2, "(" + str(round(100/phonemesSum * v,2)) + "%)", fontsize=20)
    
    
plt.show()

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Jupyter Notbook for phoneme coverage analysis

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