Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Puno Quechua
wav2vec2
Eval Results (legacy)
Instructions to use robertp408/mms-1b-all-qxp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use robertp408/mms-1b-all-qxp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="robertp408/mms-1b-all-qxp")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("robertp408/mms-1b-all-qxp") model = AutoModelForCTC.from_pretrained("robertp408/mms-1b-all-qxp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Finetuned facebook/mms-1b-all on 1502 Puno Quechua training audio samples from cmflnuzw781hfx067jwv4pt9x.
This model was created from the Mozilla.ai Blueprint: speech-to-text-finetune.
Evaluation results on 562 audio samples of Puno Quechua:
Baseline model (before finetuning) on Puno Quechua
- Word Error Rate (Normalized): 146.227
- Word Error Rate (Orthographic): 100.0
- Character Error Rate (Normalized): 332.297
- Character Error Rate (Orthographic): 344.813
- Loss: 23.528
Finetuned model (after finetuning) on Puno Quechua
- Word Error Rate (Normalized): 19.5
- Word Error Rate (Orthographic): 32.432
- Character Error Rate (Normalized): 2.718
- Character Error Rate (Orthographic): 4.452
- Loss: 0.163
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Model tree for robertp408/mms-1b-all-qxp
Base model
facebook/mms-1b-allEvaluation results
- wer on Common Voice (Puno Quechua)self-reported19.500