stcoats/whisper-large-v2-NSC

Model Card for whisper-large-v3-NSC

Model Details

Model Description

whisper-large-v3-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-v3

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

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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

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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.

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How to Get Started with the Model

from faster_whisper import WhisperModel
from huggingface_hub import snapshot_download

model_dir = snapshot_download("stcoats/whisper-large-v3-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

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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

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Compute Infrastructure

Training was conducted on the LUMI supercomputer (CSC Finland) using:

• 16 × AMD Instinct MI250X GPUs (128 GB memory each)

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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

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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.

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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)

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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)

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Citation

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}
}