README.md

---
license: cc-by-4.0
language:
  - uk
task_categories:
  - automatic-speech-recognition
  - audio-classification
tags:
  - audio
  - speech
  - automatic-speech-recognition
  - speaker-diarization
  - datasets
pretty_name: LangPipe ASR Dataset
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: train-*.parquet
dataset_info:
  features:
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
    - name: transcription
      dtype: string
    - name: transcription_original
      dtype: string
    - name: turns
      list:
        - name: speaker
          dtype: string
        - name: speaker_index
          dtype: int32
        - name: start
          dtype: float64
        - name: end
          dtype: float64
        - name: text
          dtype: string
    - name: has_pii
      dtype: bool
    - name: num_pii_objects
      dtype: int32
    - name: duration
      dtype: float64
    - name: speech_rms_dbfs
      dtype: float64
    - name: noise_rms_dbfs
      dtype: float64
    - name: snr_db
      dtype: float64
    - name: quality
      dtype: string
    - name: silence_ratio
      dtype: float64
    - name: speech_ratio
      dtype: float64
    - name: fillers
      list:
        - name: label
          dtype: string
        - name: language
          dtype: string
        - name: kind
          dtype: string
        - name: start
          dtype: float64
        - name: end
          dtype: float64
        - name: confidence
          dtype: float64
    - name: filler_count
      dtype: int32
    - name: obscene
      list:
        - name: term
          dtype: string
        - name: matched
          dtype: string
        - name: language
          dtype: string
        - name: start
          dtype: float64
        - name: end
          dtype: float64
        - name: confidence
          dtype: float64
    - name: obscene_count
      dtype: int32
    - name: code_switching
      dtype: bool
    - name: code_switching_type
      dtype: string
    - name: code_switching_confidence
      dtype: float64
    - name: code_switching_primary_language
      dtype: string
    - name: code_switching_secondary_language
      dtype: string
    - name: audio_language
      dtype: string
    - name: text_language
      dtype: string
    - name: speaker
      dtype: string
    - name: speaker_index
      dtype: int32
    - name: speaker_similarity
      dtype: float64
    - name: source_file
      dtype: string
    - name: loudness_lufs
      dtype: float64
    - name: loudness_range_lu
      dtype: float64
    - name: true_peak_dbtp
      dtype: float64
    - name: chunk_start
      dtype: float64
    - name: chunk_end
      dtype: float64
---

# test-y

`Yehor/test-y` is an audio transcription dataset for automatic speech recognition and speech-processing experiments.

The dataset contains short audio clips together with text transcriptions and metadata such as duration, quality labels, signal/noise measurements, language information, filler annotations, code-switching indicators, and anonymized speaker labels.

Users can listen to the audio samples directly in the Hugging Face Dataset Viewer.

## Dataset Structure

The dataset contains one split:

| Split | Examples |
| ----- | -------: |
| train |    1,000 |

## Columns

* `audio`: audio sample.
* `transcription`: text transcription of the audio.
* `duration`: duration of the audio clip in seconds.
* `speech_rms_dbfs`: speech loudness estimate in dBFS.
* `noise_rms_dbfs`: noise loudness estimate in dBFS.
* `snr_db`: estimated signal-to-noise ratio.
* `quality`: quality label for the audio sample.
* `silence_ratio`: ratio of silence in the clip.
* `speech_ratio`: ratio of speech in the clip.
* `fillers`: detected filler words or discourse markers.
* `filler_count`: number of detected fillers.
* `obscene`: detected obscene terms, if any.
* `obscene_count`: number of detected obscene terms.
* `code_switching`: whether code-switching is detected.
* `code_switching_type`: type of detected code-switching, if available.
* `code_switching_confidence`: confidence score for code-switching detection.
* `code_switching_primary_language`: detected primary language.
* `code_switching_secondary_language`: detected secondary language, if available.
* `audio_language`: detected language of the audio.
* `text_language`: detected language of the transcription.
* `speaker`: anonymized speaker label.
* `speaker_index`: numeric speaker index.
* `speaker_similarity`: speaker similarity score.

## Intended Uses

This dataset may be used for:

* automatic speech recognition;
* audio transcription research;
* speech quality analysis;
* multilingual and code-switching speech experiments;
* filler-word and speech-disfluency analysis.
*