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katherineahn
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Model Details
Model Provider
katherineahn
Model Tree
openai/whisper-small
Input Modalities
Output Modalities
Supported Functionality
| Model | Test WER |
|---|---|
openai/whisper-small (baseline) | 21.30% |
whisper-small-accented-en (this model) | 15.53% |
WER measured with jiwer against gold
transcripts on a speaker-disjoint held-out split (no test speaker appears in
training).
Transcription of read/prepared English speech from non-native speakers. It is a research/portfolio model and is not deployed in any production system.
python
from transformers import pipelineasr = pipeline("automatic-speech-recognition",model="katherineahn/whisper-small-accented-en",)print(asr("audio.wav")["text"])
A subset of facebook/voxpopuli,
config en_accented: European Parliament recordings from non-native English
speakers spanning 15 first-language backgrounds. Filtered to examples with gold
transcripts, then split speaker-disjoint (364 speakers, 54 held out) and capped at
3,000 training / 400 test clips. A small number of clips that failed to decode
were skipped.
Fine-tuned with the Hugging Face Seq2SeqTrainer on a single Tesla T4 GPU.
openai/whisper-small (~242M parameters)whisper-small is a small model; larger Whisper variants will transcribe more
accurately at the cost of speed and memory.Base model: Radford et al., Robust Speech Recognition via Large-Scale Weak Supervision (Whisper). Dataset: Wang et al., VoxPopuli.