Instructions to use Anujgr8/w2v-bert-malawi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Anujgr8/w2v-bert-malawi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Anujgr8/w2v-bert-malawi")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Anujgr8/w2v-bert-malawi") model = AutoModelForCTC.from_pretrained("Anujgr8/w2v-bert-malawi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
Browse files- tokenizer_config.json +0 -1
tokenizer_config.json
CHANGED
|
@@ -40,7 +40,6 @@
|
|
| 40 |
"extra_special_tokens": {},
|
| 41 |
"model_max_length": 1000000000000000019884624838656,
|
| 42 |
"pad_token": "[PAD]",
|
| 43 |
-
"processor_class": "Wav2Vec2BertProcessor",
|
| 44 |
"replace_word_delimiter_char": " ",
|
| 45 |
"target_lang": null,
|
| 46 |
"tokenizer_class": "Wav2Vec2CTCTokenizer",
|
|
|
|
| 40 |
"extra_special_tokens": {},
|
| 41 |
"model_max_length": 1000000000000000019884624838656,
|
| 42 |
"pad_token": "[PAD]",
|
|
|
|
| 43 |
"replace_word_delimiter_char": " ",
|
| 44 |
"target_lang": null,
|
| 45 |
"tokenizer_class": "Wav2Vec2CTCTokenizer",
|