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file name
string
transcript
string
duration
float64
match quality
string
hypothesis
string
CER
float64
search type
int64
ASRs
string
audio
sequence
samplerate
float64
302-27.wav
وقتی یک فرد با دوربین دوچشمی
2.477982
HIGH
وقتی یک فرد با دوربین دو چشمی
0.035714
1
['Wav2Vec']
[-0.00067138671875,-0.000152587890625,0.000335693359375,0.000885009765625,0.001434326171875,0.001708(...TRUNCATED)
44,100
302-72.wav
علاوه‌بر این، تلسکوپ‌های بی‌وپتیک
2.413991
MIDDLE
علاوه بر این تلسکپ‌های بیوپتیک
0.0625
1
['Wav2Vec']
[0.000457763671875,0.001251220703125,0.001861572265625,0.002227783203125,0.002105712890625,0.0015258(...TRUNCATED)
44,100
125-41.wav
"که به‌گفته وزیر تعاون، کار و رفاه اجتماعی، هر پنج معیا(...TRUNCATED)
7.84
HIGH
"که به گفته وزیر تعاون کار و رفاح اجتماعی هر پنج معیار ا(...TRUNCATED)
0.012821
1
['Wav2Vec']
[0.030609130859375,0.03448486328125,0.03106689453125,0.02117919921875,0.010223388671875,0.0029602050(...TRUNCATED)
44,100
131-1.wav
ابوذر سمیعی: دکتری سیاست‌گزاری فرهنگی
4.46898
MIDDLE
عبووسر سمیعی دکتری سیاست گذاری فرهنگی
0.111111
1
['Wav2Vec']
[0.003753662109375,0.00091552734375,-0.002227783203125,-0.0042724609375,-0.004791259765625,-0.004180(...TRUNCATED)
44,100
131-48.wav
به‌ویژه اگر چنین امری در کوتاه‌مدت محقق شود
4.103991
HIGH
به ویژه اگر چنین امری در کوتاه مدت محقق شود
0
1
['Wav2Vec']
[0.00018310546875,0.000579833984375,0.00091552734375,0.00091552734375,0.0006103515625,0.000305175781(...TRUNCATED)
44,100
280-7.wav
پس از دو جنگ جهانی اول و دوم،
2.532993
HIGH
پس از دو جنگ جهانی اول و دوم
0
1
['Wav2Vec']
[0.00018310546875,0.000091552734375,0.00006103515625,0.0001220703125,0.0003662109375,0.0006103515625(...TRUNCATED)
44,100
241-53.wav
"و حتی تولید نمونه‌های مشابه خارجی در داخل کشور، هنوز ا(...TRUNCATED)
10.62
HIGH
"و حتی تولید نمونه‌های مشابه خارجی در داخل کشور هنوز از(...TRUNCATED)
0.05
1
['Wav2Vec']
[0.002685546875,0.002777099609375,0.002899169921875,0.00286865234375,0.002593994140625,0.00234985351(...TRUNCATED)
44,100
241-58.wav
"و این دانش‌آموز یا از امکان داشتن معلم ویژه یا رابط مح(...TRUNCATED)
5.094989
HIGH
"و این دانش آموزیا از امکان داشتن معلم ویژه یا رابط محرو(...TRUNCATED)
0.016393
1
['Wav2Vec']
[0.00030517578125,0.000457763671875,0.000274658203125,0.000244140625,0.00054931640625,0.000610351562(...TRUNCATED)
44,100
241-70.wav
با افرادی مواجه هستیم که نوشته‌هایشان خوانا نیست.
3.436984
HIGH
با افرادی مواجه هستیم که نوشته‌هایشان خانه نیست
0.041667
1
['Wav2Vec']
[0.000152587890625,0.00018310546875,0.00018310546875,0.000030517578125,-0.000152587890625,-0.0001831(...TRUNCATED)
44,100
461-41.wav
در یک کوچه بن‌بست متوقف می‌شود.
2.860998
MIDDLE
که در یک کوچه وم بست متوقف میشود
0.2
1
['Wav2Vec']
[0.0018310546875,0.001373291015625,0.000732421875,0.000274658203125,0.000091552734375,-0.00003051757(...TRUNCATED)
44,100

ManaTTS Persian: a recipe for creating TTS datasets for lower resource languages

Hugging Face

Mana-TTS is a comprehensive and large-scale Persian Text-to-Speech (TTS) dataset designed for speech synthesis and other speech-related tasks. The dataset has been carefully collected, processed, and annotated to ensure high-quality data for training TTS models. For details on data processing pipeline and statistics, please refer to the paper in the Citation secition.

Acknowledgement

The raw audio and text files have been collected from the archive of Nasl-e-Mana magazine devoted to the blind. We thank the Nasl-e-Mana magazine for their invaluable work and for being so generous with the published dataset license. We also extend our gratitude to the Iran Blind Non-governmental Organization for their support and guidance regarding the need for open access initiatives in this domain.

Data Columns

Each Parquet file contains the following columns:

  • file name (string): The unique identifier of the audio file.
  • transcript (string): The ground-truth transcript corresponding to the audio.
  • duration (float64): Duration of the audio file in seconds.
  • match quality (string): Either "HIGH" for CER < 0.05 or "MIDDLE" for 0.05 < CER < 0.2 between actual and hypothesis transcript.
  • hypothesis (string): The best transcript generated by ASR as hypothesis to find the matching ground-truth transcript.
  • CER (float64): The Character Error Rate (CER) of the ground-truth and hypothesis transcripts.
  • search type (int64): Either 1 indicating the GT transcripts is result of Interval Search or 2 if a result of Gapped Search (refer to paper for more details).
  • ASRs (string): The Automatic Speech Recognition (ASR) systems used in order to find a satisfying matching transcript.
  • audio (sequence): The actual audio data.
  • samplerate (float64): The sample rate of the audio.

Usage

To use the dataset, you can load it directly using the Hugging Face datasets library:

from datasets import load_dataset

dataset = load_dataset("MahtaFetrat/Mana-TTS", split='train')

You can also download specific parts or the entire dataset:

# Download a specific part
wget https://huggingface.co/datasets/MahtaFetrat/Mana-TTS/resolve/main/dataset/dataset_part_01.parquet

# Download the entire dataset
git clone https://huggingface.co/datasets/MahtaFetrat/Mana-TTS

Citation

If you use Mana-TTS in your research or projects, please cite the following paper:

@article{fetrat2024manatts,
      title={ManaTTS Persian: a recipe for creating TTS datasets for lower resource languages}, 
      author={Mahta Fetrat Qharabagh and Zahra Dehghanian and Hamid R. Rabiee},
      journal={arXiv preprint arXiv:2409.07259},
      year={2024},
}

License

This dataset is available under the cc0-1.0. However, the dataset should not be utilized for replicating or imitating the speaker’s voice for malicious purposes or unethical activities, including voice cloning for malicious intent.

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