Automatic Speech Recognition
Transformers
Safetensors
Twi
Akan
whisper
akan
twi
ghanaian-speech-lab
serendepify-gsl
Instructions to use teckedd/serendepify-gsl-asr-ak-waxal-gnlp-whisper-small-broad-lowlr-freezeenc-fullft-v0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use teckedd/serendepify-gsl-asr-ak-waxal-gnlp-whisper-small-broad-lowlr-freezeenc-fullft-v0.3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="teckedd/serendepify-gsl-asr-ak-waxal-gnlp-whisper-small-broad-lowlr-freezeenc-fullft-v0.3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("teckedd/serendepify-gsl-asr-ak-waxal-gnlp-whisper-small-broad-lowlr-freezeenc-fullft-v0.3") model = AutoModelForSpeechSeq2Seq.from_pretrained("teckedd/serendepify-gsl-asr-ak-waxal-gnlp-whisper-small-broad-lowlr-freezeenc-fullft-v0.3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
serendepify-gsl-asr-ak-waxal-gnlp-whisper-small-broad-lowlr-freezeenc-fullft-v0.3
This is the third Ghanaian Speech Lab ASR artifact: a broader Waxal+GhanaNLP low-learning-rate full fine-tuning pass with the Whisper encoder frozen.
It is a real training run, not a smoke-test checkpoint, but it is still a review candidate until the held-out evaluation and failure taxonomy are complete.
License is set conservatively because this pass includes Waxal-derived data.
- Training rows: 12000
- Dev rows: 512
- Max steps: 800
- Method: full fine-tuning from
teckedd/whisper-small-waxal-round2-specaug-v1 - Dataset mix: broad Waxal + GhanaNLP from the v0.1 sanitized manifest
- Frozen encoder: True
- Stop on first regression: True
Baseline metrics:
{
"baseline_loss": 5.13262939453125,
"baseline_model_preparation_time": 0.0046,
"baseline_wer": 0.4543915741959752,
"baseline_runtime": 238.4489,
"baseline_samples_per_second": 2.147,
"baseline_steps_per_second": 0.537
}
Baseline by corpus:
{
"waxal": {
"baseline_waxal_loss": 3.3425865173339844,
"baseline_waxal_model_preparation_time": 0.0046,
"baseline_waxal_wer": 0.3454323454323454,
"baseline_waxal_runtime": 158.7616,
"baseline_waxal_samples_per_second": 1.612,
"baseline_waxal_steps_per_second": 0.403
},
"gnlp": {
"baseline_gnlp_loss": 6.922672271728516,
"baseline_gnlp_model_preparation_time": 0.0046,
"baseline_gnlp_wer": 1.0584615384615386,
"baseline_gnlp_runtime": 76.1024,
"baseline_gnlp_samples_per_second": 3.364,
"baseline_gnlp_steps_per_second": 0.841
}
}
Final metrics:
{
"final_loss": 1.140019416809082,
"final_model_preparation_time": 0.0046,
"final_wer": 0.451758510438217,
"final_runtime": 223.9252,
"final_samples_per_second": 2.286,
"final_steps_per_second": 0.572,
"epoch": 0.5333333333333333
}
Final by corpus:
{
"waxal": {
"final_waxal_loss": 0.498562753200531,
"final_waxal_model_preparation_time": 0.0046,
"final_waxal_wer": 0.3584193584193584,
"final_waxal_runtime": 154.7631,
"final_waxal_samples_per_second": 1.654,
"final_waxal_steps_per_second": 0.414,
"epoch": 0.5333333333333333
},
"gnlp": {
"final_gnlp_loss": 1.7814760208129883,
"final_gnlp_model_preparation_time": 0.0046,
"final_gnlp_wer": 0.9692307692307692,
"final_gnlp_runtime": 71.1072,
"final_gnlp_samples_per_second": 3.6,
"final_gnlp_steps_per_second": 0.9,
"epoch": 0.5333333333333333
}
}
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Model tree for teckedd/serendepify-gsl-asr-ak-waxal-gnlp-whisper-small-broad-lowlr-freezeenc-fullft-v0.3
Base model
openai/whisper-small