README.md

Open-speech

open-speech is a collection of popular speech datasets. Datasets included in the collection are:

• Mozilla Common Voice Dataset (cv-corpus-5.1-2020-06-22)
• VoxForge
• LibriSpeech

Datasets have been pre-processed as follows:

• Audio files have been resampled to 16kHz.
• Audio files longer than 68kB (~21.25 seconds) have been discarded.
• Data has been sharded into ~256MB TFRecord files.

If you find this project useful, please consider a small donation to help me pay for data storage:

Usage examples

open-speech can be used as either one large dataset or individual datasets can be accessed and used on their own.

Get data on each dataset:

import open_speech

for dataset in open_speech.datasets:

    print("         name:", dataset.name)
    print("  sample_rate:", dataset.sample_rate)
    print("        dtype:", dataset.dtype)
    print("   # of files:", len(dataset.files))
    print("# of examples:",
        "train=", len(dataset.train_labels),
        "valid=", len(dataset.valid_labels), "test=", len(dataset.test_labels)
    )
    print()

Output:

         name: common_voice
  sample_rate: 16000
        dtype: <dtype: 'float32'>
   # of files: 631
# of examples: train= 435943 valid= 16028 test= 16012

         name: voxforge
  sample_rate: 16000
        dtype: <dtype: 'float32'>
   # of files: 108
# of examples: train= 76348 valid= 9534 test= 9553

         name: librispeech
  sample_rate: 16000
        dtype: <dtype: 'float32'>
   # of files: 450
# of examples: train= 132542 valid= 2661 test= 2558

Use entire collection as one large dataset:

import open_speech
import tensorflow as tf

print("  sample_rate:", open_speech.sample_rate)
print("        dtype:", open_speech.dtype)
print("   # of files:", len(open_speech.files))
print("# of examples:",
    "train=", len(open_speech.train_labels),
    "valid=", len(open_speech.valid_labels), "test=", len(open_speech.test_labels)
)
print()

# get a clean set of labels:
#    - convert unicode characters to their ascii equivalents
#    - strip leading and trailing whitespace
#    - convert to lower case
#    - strip all punctuation except for the apostrophe (')
#
clean_labels = {
    uuid: open_speech.clean(label) for uuid, label in open_speech.labels.items()
}

chars = set()
for label in clean_labels.values(): chars |= set(label)
print("alphabet:", sorted(chars))

max_len = len(max(clean_labels.values(), key=len))
print("longest sentence:", max_len, "chars")
print()

def transform(dataset):
    # use open_speech.parse_serial to de-serialize examples;
    # this function will return tuples of (uuid, audio)
    dataset = dataset.map(open_speech.parse_serial)

    # use open_speech.lookup_table to look up and replace uuids
    # with corresponding labels
    table = open_speech.lookup_table(clean_labels)
    dataset = dataset.map(lambda uuid, audio: (audio, table.lookup(uuid)))

    # ... do something ...

    return dataset

train_dataset = transform( open_speech.train_recordset )
valid_dataset = transform( open_speech.valid_recordset )

hist = model.fit(x=train_dataset, validation_data=valid_dataset,
    # ... other parameters ...
)

test_dataset = transform( open_speech.test_recordset )

loss, metrics = model.evaluate(x=test_dataset,
    # ... other parameters ...
)

Output:

  sample_rate: 16000
        dtype: <dtype: 'float32'>
   # of files: 1189
# of examples: train= 644833 valid= 28223 test= 28123

alphabet: [' ', "'", '0', '1', '2', '3', '4', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
longest sentence: 398 chars

...

Use individual dataset

import open_speech
from open_speech import common_voice
import tensorflow as tf

print("name:", common_voice.name)
print("sample_rate:", common_voice.sample_rate)
print("dtype:", common_voice.dtype)

def transform(dataset):
    # use open_speech.parse_serial to de-serialize examples;
    # this function will return tuples of (uuid, audio)
    dataset = dataset.map(open_speech.parse_serial)

    # use open_speech.lookup_table to look up and replace uuids
    # with corresponding labels
    table = open_speech.lookup_table(common_voice.labels)
    dataset = dataset.map(lambda uuid, audio: (audio, table.lookup(uuid)))

    # ... do something ...

    return dataset

train_dataset = transform( common_voice.train_recordset )
valid_dataset = transform( common_voice.valid_recordset )

hist = model.fit(x=train_dataset, validation_data=valid_dataset,
    # ... other parameters ...
)

Output:

name: common_voice
sample_rate: 16000
dtype: <dtype: 'float32'>

...

Authors

• Dimitry Ishenko - dimitry (dot) ishenko (at) (gee) mail (dot) com

License

This project is distributed under the GNU GPL license. See the LICENSE.md file for details.