Automatic Speech Recognition
Transformers
PyTorch
Chinese
wav2vec2
audio
speech
xlsr-fine-tuning-week
Instructions to use gymeee/demo_code_switching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gymeee/demo_code_switching with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="gymeee/demo_code_switching")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("gymeee/demo_code_switching") model = AutoModelForCTC.from_pretrained("gymeee/demo_code_switching", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download eval_results.json from gymeee/demo_code_switching: direct link, hf CLI and curl.
- Browser
- Download file 260 Bytes
-
https://huggingface.co/gymeee/demo_code_switching/resolve/main/eval_results.json
- Command line
-
hf download hf://gymeee/demo_code_switching/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/gymeee/demo_code_switching/resolve/main/eval_results.json
260 Bytes
| { | |
| "epoch": 30.0, | |
| "eval_cer": 0.25046854726320344, | |
| "eval_loss": 1.9711192846298218, | |
| "eval_mer": 0.2958612975391499, | |
| "eval_runtime": 215.4984, | |
| "eval_samples": 1127, | |
| "eval_samples_per_second": 5.23, | |
| "eval_steps_per_second": 0.654 | |
| } |