---
language:
- pl
task_categories:
- automatic-speech-recognition
tags:
- asr
- evaluation
- polish
- houseplants
- botany
size_categories:
- n<1K
---

# Dataset Card: Group 4 - ZWESUI0 (Houseplants)

## Dataset Description
- **Repository:** `mszulcc/grupa-4-ZWESUI0`
- **Language:** Polish (pl)
- **Domain:** Indoor plant cultivation (Houseplants / Botany)
- **Size:** 523 segments

### Dataset Summary
A dataset created for the final project of the Speech Recognition Systems Evaluation Workshop (ZWESUI). The corpus focuses on the evaluation of ASR systems in the specific domain of **houseplants and botany**. 

The dataset contains 523 audio segments originating from two main sources with different acoustic and linguistic characteristics:
1. **Spontaneous speech (YouTube):** Excerpts from educational and tutorial videos on plant cultivation.
2. **Synthetic speech (TTS):** Read speech generated from botanical texts using modern TTS engines (Coqui XTTS v2, KugelAudio-0-Open).

### Data Breakdown
The corpus contains approximately **1 hour and 6 minutes** (~66.6 minutes) of audio data:
- **Total Duration:** ~3996 seconds
- **Spontaneous Speech (YouTube):** 271 segments (~43.2 minutes)
- **Synthetic Speech (TTS Total):** 252 segments (~23.4 minutes)
  - *KugelAudio-0-Open:* 151 segments
  - *Coqui XTTS v2:* 101 segments

### Annotator Breakdown
The dataset was manually prepared and cross-verified by a 5-person team. Following the project requirements, each team member was responsible for transcribing/generating and cross-verifying an assigned pool of recordings:

**Transcription / Generation (`annotator`):**
- `s463015`: 121 segments
- `511023`: 102 segments
- `s512479`: 100 segments
- `481875`: 100 segments
- `s452376`: 100 segments

**Cross-validation (`verified_by`):**
- `s512479`: 121 segments
- `s452376`: 102 segments
- `s463015`: 100 segments
- `511023`: 100 segments
- `481875`: 100 segments

### Tools & Methodology
The process of compiling and processing the dataset relied on the following tools and technologies:
- **Audio Generation (TTS):** Coqui XTTS v2 and KugelAudio-0-Open engines.
- **Text Normalization:** A custom Python pipeline (`normalize.py`) based on regular expressions, enforcing a uniform representation of numbers, abbreviations, and punctuation, fully documented in the normalization protocol.
- **Data Engineering:** `pandas` and the HuggingFace `datasets` ecosystem were used to structure the audio files and transcriptions into the final format.
- **ASR Evaluation:** Testing was conducted on infrastructure utilizing the MLX backend (Apple Silicon) for local execution of the Qwen3-ASR model, alongside standard `transformers` libraries for Whisper and API calls for Cohere.

### Supported Tasks
- `automatic-speech-recognition`: Evaluation, testing, and comparison of speech recognition systems for the Polish language, with an emphasis on specialized vocabulary (plant names, care treatments).

## Dataset Structure

### Data Fields
The dataset provides a standard column layout for the `datasets` library:
- `audio`: Audio recording in WAV format (usually 16kHz, mono).
- `text`: Original reference transcription.
- `text_norm`: Transcription after a rigorous text normalization process (using rules prepared in the `normalize.py` file).
- `source`: The origin source of the recording (e.g., `youtube`, `coqui_xtts_v2`, `KugelAudio-0-Open`).
- `duration`: The length of the audio segment in seconds.
- `source_license`: The license assigned to a given segment resulting from source rights (e.g., `CC-BY 3.0` for YouTube, `MIT` for KugelAudio).

## Evaluation & Baseline
The dataset was used to conduct a multidimensional evaluation of 3 ASR systems with different architectures. Results for the entire dataset (`n=523`) applying normalization for both references and hypotheses:

| System | WER [%] | CER [%] | Bootstrap CI 95% (WER) |
| :--- | :---: | :---: | :---: |
| **Whisper-large-v3** | 7.67 | 3.02 | 6.83 - 8.48 |
| **Cohere-Transcribe-03-2026** | 8.43 | 3.04 | 7.60 - 9.27 |
| **Qwen3-ASR-1.7B (MLX)** | 15.61 | 5.26 | 14.58 - 16.55 |

**Quality Gap (Main observations):**
Error analysis revealed the largest quality gap between spontaneous and read speech. All systems (including Whisper-large-v3) show a significantly higher error rate (WER) for spontaneous speech segments from YouTube compared to highly regular, phonetically clean recordings from TTS engines.

## Licensing
The corpus is a compilation of data from various sources, and each retains its original license:
- Recordings obtained from YouTube are shared under the **Creative Commons CC-BY 3.0** license (as the platform's standard Creative Commons license).
- Generated synthetic recordings inherit the license of the respective models (**MIT** for KugelAudio-0-Open, Coqui Public Model License for Coqui XTTS v2). 

Detailed license information for each of the 523 segments can be found in the `source_license` column.