| --- |
| license: mit |
| language: |
| - en |
| pipeline_tag: automatic-speech-recognition |
| tags: |
| - speech-recognition |
| - whisper |
| - singapore-english |
| - sociolinguistics |
| - corpus-construction |
| - domain-adaptation |
| base_model: |
| - openai/whisper-large-v2 |
| --- |
| |
| # Model Card for `whisper-large-v3-NSC` |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| `whisper-large-v2-NSC` is a domain-adapted automatic speech recognition (ASR) model for conversational Singapore English (SgE). |
| It is a fine-tuned version of OpenAI’s Whisper Large-V3 model trained on aligned conversational speech from Part 3 of the National Speech Corpus (NSC). The model is optimized to improve the recoverability of interactional, morphosyntactic, and discourse-pragmatic features characteristic of contemporary spoken Singapore English. |
|
|
| The model was developed as part of a workflow for constructing **YCSEP_v2 (YouTube Corpus of Singapore English Podcasts, version 2)**, demonstrating how targeted fine-tuning can improve linguistic fidelity in large-scale corpus creation. |
| |
| - **Developed by:** Steven Coats, Carmelo Alessandro Basile, Cameron Morin, Robert Fuchs |
| - **Funded by:** EU NextGenerationEU / Research Council of Finland (grant 358720) |
| - **Model type:** Automatic Speech Recognition (seq2seq Transformer) |
| - **Language(s):** English (Singapore English) |
| - **License:** MIT |
| - **Finetuned from model:** `openai/whisper-large-v2` |
| |
| ### Model Sources |
| |
| - **Related publication:** Coats et al. (2025), *The YouTube Corpus of Singapore English Podcasts*, English World-Wide |
| - **Corpus produced with the model:** YCSEP_v2 |
| |
| --- |
| |
| ## Uses |
| |
| ### Direct Use |
| |
| The model is intended for: |
| |
| - Transcription of conversational Singapore English speech |
| - Linguistic corpus construction and annotation workflows |
| - Research in World Englishes, sociolinguistics, and interactional linguistics |
| - Recovering discourse particles and local morphosyntax often missed by general ASR systems |
| |
| ### Downstream Use |
| |
| The model can be integrated into pipelines involving: |
| |
| - Speaker diarization (e.g., WhisperX + pyannote) |
| - POS tagging and syntactic annotation (e.g., spaCy pipelines) |
| - Construction-grammar-based corpus analysis |
| - Speech-based sociolinguistic research |
| |
| ### Out-of-Scope Use |
| |
| This model is **not optimized for**: |
| |
| - Standard American/British broadcast speech |
| - Multilingual ASR outside the Singapore English domain |
| - Real-time or low-latency applications |
| - Legal, medical, or safety-critical transcription |
| |
| --- |
| |
| ## Bias, Risks, and Limitations |
| |
| - The model is trained narrowly on conversational Singapore English and may underperform on other English varieties. |
| - Training data reflects the demographic distribution of NSC Part 3 and is not fully balanced sociolinguistically. |
| - Domain specialization may bias outputs toward informal conversational registers. |
| - The model is designed for research and corpus-building, not general-purpose ASR deployment. |
| |
| ### Recommendations |
| |
| Users should evaluate performance carefully before applying the model outside conversational Singapore English contexts. |
| |
| --- |
| |
| ## How to Get Started with the Model |
| |
| ```python |
| from faster_whisper import WhisperModel |
| from huggingface_hub import snapshot_download |
| |
| model_dir = snapshot_download("stcoats/whisper-large-v2-NSC") |
| model = WhisperModel(model_dir, device="cuda", compute_type="float16") |
| ``` |
| |
| ## Training Details |
| |
| ### Training Data |
| |
| Training data was derived from **National Speech Corpus (NSC) Part 3**, consisting of same-room conversational recordings between two speakers (friends, partners, or family members). |
| |
| Processing steps included: |
| |
| - Alignment of WAV recordings with Praat TextGrid transcripts |
| - Removal of markup and non-speech symbols |
| - Merging adjacent utterances separated by ≤ 0.25 s pauses |
| - Segmentation into 10–30 second conversational chunks |
| - Export of aligned 16-bit PCM WAV segments with metadata (JSONL format) |
| |
| **Dataset statistics:** |
| |
| | Measure | Value | |
| |----------------|-------------| |
| | Speakers | 428 | |
| | Segments | 69,603 | |
| | Words | 4.57 million| |
| | Audio length | 458 hours | |
| |
| **Data split:** 80% training / 20% evaluation |
| |
| --- |
| |
| ### Training Procedure |
| |
| Six Whisper model sizes were fine-tuned; this released model corresponds to the best-performing configuration. |
| |
| #### Training Hyperparameters |
| |
| - Learning rate: 2.5e-6 |
| - Weight decay: 0.02 |
| - Warm-up steps: 300 |
| - Effective batch size: 16 |
| - Epochs: 8 |
| - Logging interval: 200 steps |
| - Checkpoint interval: 1,000 steps |
| - Training regime: bf16 mixed precision |
| |
| --- |
| |
| ### Compute Infrastructure |
| |
| Training was conducted on the **LUMI supercomputer (CSC Finland)** using: |
| |
| - 16 × AMD Instinct MI250X GPUs (128 GB memory each) |
| |
| --- |
| |
| ## Evaluation |
| |
| ### Testing Data |
| |
| Two evaluation datasets were used: |
| |
| - MERaLiON NSC-derived test set |
| - Random 1,000-segment sample from held-out NSC data |
| |
| ### Metrics |
| |
| - **WER (Word Error Rate)** |
| - **CER (Character Error Rate)** |
| |
| These metrics assess transcription fidelity for conversational speech. |
| |
| ### Results |
| |
| Fine-tuning produced substantial gains across all Whisper variants. |
| The best fine-tuned model slightly outperformed MERaLiON-2-10B-ASR despite being substantially smaller. |
| |
| **Example comparison (NSC sample):** |
| |
| | Model | WER | CER | |
| |-----------------------------|--------|--------| |
| | whisper-large-v3 (baseline) | 0.3302 | 0.2452 | |
| | whisper-large-v2-ft | 0.2245 | 0.1599 | |
| | MERaLiON-2-10B-ASR | 0.2553 | 0.1844 | |
| |
| --- |
| |
| ## Model Examination |
| |
| The model improves recoverability of interactional and morphosyntactic structure, enabling more reliable extraction of constructions and discourse particles in YCSEP_v2 and related linguistic analyses. |
| |
| --- |
| |
| ## Environmental Impact |
| |
| Training used shared HPC infrastructure (LUMI), which operates with a high proportion of renewable energy. |
| |
| - **Hardware:** AMD MI250X GPU cluster |
| - **Training duration:** 8 epochs across 16 GPUs |
| - **Compute region:** Finland (CSC) |
|
|
| --- |
|
|
| ## Technical Specifications |
|
|
| ### Architecture and Objective |
|
|
| Sequence-to-sequence Transformer ASR model based on the Whisper architecture, optimized through supervised fine-tuning on aligned conversational speech. |
|
|
| ### Software |
|
|
| - PyTorch |
| - Hugging Face Transformers |
| - WhisperX + pyannote (for downstream corpus creation) |
| - spaCy 3.8 (linguistic annotation) |
|
|
| --- |
|
|
| ## Citation |
|
|
| **BibTeX** |
|
|
| ```bibtex |
| @article{coats2025ycsep, |
| author = {Coats, Steven and Basile, Carmelo Alessandro and Morin, Cameron and Fuchs, Robert}, |
| title = {The YouTube Corpus of Singapore English Podcasts}, |
| journal = {English World-Wide}, |
| year = {2025}, |
| volume = {46}, |
| number = {3}, |
| pages = {274--298}, |
| doi = {10.1075/eww.25018.coa} |
| } |
