task_categories:automatic-speech-recognition task_categories:audio-classification task_categories:text-to-speech task_categories:text-to-audio language:ro license:mit size_categories:10K<n<100K format:csv modality:audio modality:text library:datasets library:pandas library:mlcroissant library:polars region:us speech speech-transcription romanian
---
tags:
- speech
- speech-transcription
- romanian
language:
- ro
license: mit
task_categories:
- automatic-speech-recognition
- audio-classification
- text-to-speech
- text-to-audio
pretty_name: RO_CV20
size_categories:
- 10K<n<100K
---
# Common Voices Corpus 20.0 (Romanian)
<h5 style="font-family: 'Calibri'; margin-bottom: 20px;">
Common Voices is an open-source dataset of speech recordings created by 
<a href="https://commonvoice.mozilla.org" target="_blank">Mozilla</a> to improve speech recognition technologies. 
It consists of crowdsourced voice samples in multiple languages, contributed by volunteers worldwide.
</h5>

<h5 style="font-family: 'Calibri'; margin-bottom: 20px;">
<strong>Challenges:</strong> The raw dataset included numerous recordings with incorrect transcriptions 
or those requiring adjustments, such as sampling rate modifications, conversion to .wav format, and other refinements 
essential for optimal use in developing and fine-tuning various models.
</h5>

<h5 style="font-family: 'Calibri'; margin-bottom: 20px;">
<strong>Processing:</strong> Our team, led by project manager Ionuț Vișan, carefully reviewed and manually corrected the 
transcriptions of all audio segments, ensuring their conversion into the required format for modern models 
(16k Hz sampling rate, mono channel, .wav format).
</h5>

---
<h2>Dataset Summary<h2>
  
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<strong>common_voices20_audio.zip: </strong> The archive containing all processed audio segments.
</h5>
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Total number of audio segments: <strong>41,431</strong>.
</h5>
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Total duration of all audio segments combined: approximately <strong>47 hours</strong>.
</h5>

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<strong>common_voices20.csv: </strong> Contains metadata for all segments from the common_voices20_audio.
</h5>
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The file contains 41,431 rows and 2 columns:
</h5>
<h5 style="font-family: 'Calibri'; margin-bottom: 2px;">
  <ul>
    <li><em>audio_file</em>: File names of the processed audio segments from common_voices20_audio.</li>
    <li><em>transcript</em>: Corresponding text transcriptions for each audio file from common_voices20_audio.</li>
  </ul>
</h5>

---
<h2>Split<h2>
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To split the dataset (common_voices20.csv), we performed an 80-20 split into training and test sets using a seed value of 42, resulting in:
</h5>
<h5 style="font-family: 'Calibri'; margin-bottom: 2px;">
  <ul>
    <li><em>train_common_voices20.csv</em>: It contains 33,144 of the audio segments.</li>
    <li><em>test_common_voices20.csv</em>: It contains 8,287 of the audio segments.</li>
  </ul>
</h5>

---
<h2>How to use<h2>

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<strong></strong>If you want to download all the files from the dataset, use the following code:
</h5>

<details>
  <summary><strong>Click to expand the code</strong></summary>

```python
from huggingface_hub import hf_hub_download
import zipfile
import os

# Repo and files
Dataset = "TransferRapid/CommonVoices20_ro"

filenames = [
    "common_voices20.csv",
    "test_common_voices20.csv",
    "train_common_voices20.csv",
    "common_voices20_audio.zip"
]

# Download files
for filename in filenames:
    print(f"Downloading {filename}...")
    file_path = hf_hub_download(repo_id=Dataset, filename=filename, repo_type="dataset", local_dir="./")
    print(f"Downloaded {filename} to: {file_path}")
    
    # Extract ZIP files
    if filename.endswith('.zip'):
        extracted_dir = filename.replace('.zip', '')
        with zipfile.ZipFile(file_path, 'r') as zip_ref:
            zip_ref.extractall(extracted_dir)
        print(f"Extracted files to: {extracted_dir}")
        print(os.listdir(extracted_dir))
    else:
        print(f"{filename} is available.")

```
</details>

---
<h2>Usage<h2>
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The dataset can be used for:
</h5>
<h5 style="font-family: 'Calibri'; margin-bottom: 2px;">
  <ul>
    <li><em>Speech-to-Text (STT) – Automatic Transcription</em></li>
    <li><em>Text-to-Speech (TTS) – Synthetic Voice Generation</em></li>
    <li><em>Speech Enhancement & Noise Reduction</em></li>
    <li><em>Speaker Recognition & Verification</em></li>
    <li><em>Sentiment Analysis & Emotion Recognition</em></li>
    <li><em>AI-Powered Voice Assistants & Smart Devices</em></li>
  </ul>
</h5>

---
<h2>Communication<h2>
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For any questions regarding this dataset or to explore collaborations on ambitious AI/ML projects, please feel free to contact us at:
</h5>
<h5 style="font-family: 'Calibri'; margin-bottom: 2px;">
  <ul>
    <li><em>ionut.visan@transferrapid.com</em></li>
    <li><em><a href="https://www.linkedin.com/in/ionut-visan/" target="_blank">Ionuț Vișan's Linkedin</a></em></li>
    <li><em><a href="https://www.linkedin.com/company/transfer-rapid" target="_blank">Transfer Rapid's Linkedin</a></em></li>
  </ul>
</h5>
  </ul>
</h5>