README.md

---
license: other
tags:
- arabic
- speech
- asr
- tts
- audio
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: transcription
    dtype: string
  - name: model
    dtype: string
  - name: timestamps
    struct:
    - name: word
      list: string
    - name: start
      list: float64
    - name: end
      list: float64
  - name: speakerandsession
    dtype: string
  - name: origin
    dtype: string
  splits:
  - name: train
    num_bytes: 1578261081
    num_examples: 7568
  - name: validation
    num_bytes: 5070827
    num_examples: 15
  download_size: 1580192130
  dataset_size: 1583331908
---

# Arabic Speech Dataset

A curated Arabic speech dataset combining three sources, transcribed via Whisper-large-v3 (primary) and omniASR-7B (fallback). Built for Arabic TTS and ASR training.

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("KFUPM-JRCAI/arabic_speech", split="train")
print(ds[0]["transcription"])         # ASR text
print(ds[0]["audio"]["array"])        # audio waveform (float32, 16kHz)
print(ds[0]["timestamps"])            # word-level timestamps (nullable)
print(ds[0]["model"])                 # "whisper-large-v3" or "omniASR-7B"
print(ds[0]["origin"])                # source dataset name
print(ds[0]["speakerandsession"])     # "session_id::speaker"
```

## Schema

| Column | Type | Description |
|--------|------|-------------|
| audio | Audio(16000) | Decoded mono waveform at 16kHz |
| transcription | string | Raw ASR output (not Gemini-normalized) |
| model | string | "whisper-large-v3" or "omniASR-7B" |
| timestamps | List[{word, start, end}] | Word-level timestamps (nullable) |
| speakerandsession | string | {session_id}::{speaker} |
| origin | string | Source HF dataset name |

## Data Sources

| Source | Origin | Rows | Description |
|--------|--------|------|-------------|
| ArabicVoicesClean_v5 | [KFUPM-JRCAI/ArabicVoicesClean_v5](https://huggingface.co/datasets/KFUPM-JRCAI/ArabicVoicesClean_v5) | 2,961 | Crowd-sourced Arabic speech recordings |
| Miro | [TigreGotico/tts-train-synthetic-miro_ar-diacritics](https://huggingface.co/datasets/TigreGotico/tts-train-synthetic-miro_ar-diacritics) | 237 | TTS-synthetic Arabic with diacritics |
| DII | [TigreGotico/tts-train-synthetic-dii_ar-diacritics](https://huggingface.co/datasets/TigreGotico/tts-train-synthetic-dii_ar-diacritics) | 4,350 | TTS-synthetic Arabic with diacritics |

**Total: 7,548 rows** (6,352 whisper / 1,196 omniASR).

## How It Was Built

The pipeline starts from the original datasets (audio + text pairs), then applies automatic speech recognition (ASR) to filter and enrich the data:

### 1. ASR Transcription & Filtering

For each audio-text pair from the original datasets, we run **Whisper-large-v3** (and omniASR-7B as a fallback) to produce an ASR transcript. The ASR transcript is then compared against the original text:

- **Rows where the ASR output matches the original text well** are marked `keep=true` in filtered_records.jsonl -- these are the high-quality pairs retained in this dataset.
- **Rows where the ASR deviates significantly** are discarded. The original text may contain errors (e.g., mismatched audio), and the ASR serves as a quality gate.

### 2. Word-Level Timestamps

For retained rows, whisper also produces word-level timestamps (word, start, end) stored in asr_words_cache.jsonl. These enable alignment tasks and fine-grained analysis.

### 3. Union (Whisper-preferred)

When both whisper and omniASR transcriptions exist for a row, the whisper version is used. omniASR is only used when whisper coverage is missing (1,196 rows).

### 4. Clip ID Mapping

HuggingFace Dataset loads wav files in alphabetical order (1.wav, 10.wav, 100.wav, ...), so a naive ds_idx + 1 mapping maps audio to the wrong transcription. To fix this, each row's original text is matched against metadata.csv to find the real clip_id.

### 5. Audio Loading

- **ArabicVoicesClean_v5**: Audio decoded from parquet shards (embedded binary WAV) via soundfile.read at 16kHz.
- **Miro/DII**: Audio loaded from complete wav directories at /tmp/tts-train-synthetic-*-hf/wav/.

## Known Limitations

### Missing Word Timestamps

The word-level timestamp generation (asr_words_cache.jsonl) was only run for a subset of indices:
- Miro: indices 1-383 (237/570 keep rows have timestamps)
- DII: indices 1-7,943 (4,333/5,559 keep rows have timestamps)
- ArabicVoicesClean_v5: 100% covered

**How to fix**: Re-run the word-segmentation step on the remaining indices. The asr_text_cache.jsonl has full coverage -- only the timestamp extraction was interrupted.

### Incomplete Miro Coverage

The miro ASR was only run on the first 968 clip_ids (indices 0-967) out of ~9,994 total. To expand coverage, re-run ASR inference on the remaining rows and include them in the keep set.

## License

Refer to the licenses of the individual source datasets:
- [KFUPM-JRCAI/ArabicVoicesClean_v5](https://huggingface.co/datasets/KFUPM-JRCAI/ArabicVoicesClean_v5)
- [TigreGotico/tts-train-synthetic-miro_ar-diacritics](https://huggingface.co/datasets/TigreGotico/tts-train-synthetic-miro_ar-diacritics)
- [TigreGotico/tts-train-synthetic-dii_ar-diacritics](https://huggingface.co/datasets/TigreGotico/tts-train-synthetic-dii_ar-diacritics)