Automatic Speech Recognition
Transformers
Safetensors
Twi
Akan
whisper
akan
twi
ghanaian-speech-lab
serendepify-gsl
Instructions to use teckedd/serendepify-gsl-asr-ak-gnlp-whisper-small-only-lowlr-freezeenc-fullft-v0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use teckedd/serendepify-gsl-asr-ak-gnlp-whisper-small-only-lowlr-freezeenc-fullft-v0.5 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-gnlp-whisper-small-only-lowlr-freezeenc-fullft-v0.5")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("teckedd/serendepify-gsl-asr-ak-gnlp-whisper-small-only-lowlr-freezeenc-fullft-v0.5") model = AutoModelForSpeechSeq2Seq.from_pretrained("teckedd/serendepify-gsl-asr-ak-gnlp-whisper-small-only-lowlr-freezeenc-fullft-v0.5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
serendepify-gsl-asr-ak-gnlp-whisper-small-only-lowlr-freezeenc-fullft-v0.5
This is a Ghanaian Speech Lab ASR artifact: a GhanaNLP-only low-learning-rate full fine-tuning pass from the Round 2 checkpoint 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 is part of the Akan ASR lab's traceable diagnostic sequence.
- Training rows: 10000
- Dev rows: 602
- Max steps: 800
- Method: full fine-tuning from
teckedd/whisper-small-waxal-round2-specaug-v1 - Dataset mix: GhanaNLP only for training; Waxal dev is used only as a regression check
- Frozen encoder: True
- Stop on first regression: True
Baseline metrics:
{
"baseline_loss": 6.963376522064209,
"baseline_model_preparation_time": 0.0047,
"baseline_wer": 1.1397413137205175,
"baseline_runtime": 199.9928,
"baseline_samples_per_second": 3.01,
"baseline_steps_per_second": 0.755
}
Baseline by corpus:
{
"gnlp": {
"baseline_gnlp_loss": 6.963376522064209,
"baseline_gnlp_model_preparation_time": 0.0047,
"baseline_gnlp_wer": 1.1397413137205175,
"baseline_gnlp_runtime": 199.2396,
"baseline_gnlp_samples_per_second": 3.021,
"baseline_gnlp_steps_per_second": 0.758
},
"waxal_regression": {
"baseline_waxal_regression_loss": 3.284214496612549,
"baseline_waxal_regression_model_preparation_time": 0.0047,
"baseline_waxal_regression_wer": 0.32592715414801304,
"baseline_waxal_regression_runtime": 321.2727,
"baseline_waxal_regression_samples_per_second": 1.594,
"baseline_waxal_regression_steps_per_second": 0.398
}
}
Final metrics:
{
"final_loss": 1.3758597373962402,
"final_model_preparation_time": 0.0047,
"final_wer": 0.8754755262490489,
"final_runtime": 174.409,
"final_samples_per_second": 3.452,
"final_steps_per_second": 0.866,
"epoch": 1.28
}
Final by corpus:
{
"gnlp": {
"final_gnlp_loss": 1.3758597373962402,
"final_gnlp_model_preparation_time": 0.0047,
"final_gnlp_wer": 0.8754755262490489,
"final_gnlp_runtime": 174.0939,
"final_gnlp_samples_per_second": 3.458,
"final_gnlp_steps_per_second": 0.867,
"epoch": 1.28
},
"waxal_regression": {
"final_waxal_regression_loss": 0.5538806915283203,
"final_waxal_regression_model_preparation_time": 0.0047,
"final_waxal_regression_wer": 0.357541590670425,
"final_waxal_regression_runtime": 327.7075,
"final_waxal_regression_samples_per_second": 1.562,
"final_waxal_regression_steps_per_second": 0.391,
"epoch": 1.28
}
}
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Model tree for teckedd/serendepify-gsl-asr-ak-gnlp-whisper-small-only-lowlr-freezeenc-fullft-v0.5
Base model
openai/whisper-small