Instructions to use nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based") model = AutoModelForCTC.from_pretrained("nrshoudi/wav2vec2-large-xls-r-300m-Arabic-phoneme-based", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 0
Browse files- config.json +1 -1
- pytorch_model.bin +1 -1
- training_args.bin +1 -1
config.json
CHANGED
|
@@ -102,7 +102,7 @@
|
|
| 102 |
1
|
| 103 |
],
|
| 104 |
"torch_dtype": "float32",
|
| 105 |
-
"transformers_version": "4.
|
| 106 |
"use_weighted_layer_sum": false,
|
| 107 |
"vocab_size": 48,
|
| 108 |
"xvector_output_dim": 512
|
|
|
|
| 102 |
1
|
| 103 |
],
|
| 104 |
"torch_dtype": "float32",
|
| 105 |
+
"transformers_version": "4.31.0",
|
| 106 |
"use_weighted_layer_sum": false,
|
| 107 |
"vocab_size": 48,
|
| 108 |
"xvector_output_dim": 512
|
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1262098669
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4e584a31f1c97bf2c64e0877d16bf01445069d1846c49db2a73b852d820a0424
|
| 3 |
size 1262098669
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 4027
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e8d366763718b419138c144dd6b4a474001ad6269a166e34d82a943933c53295
|
| 3 |
size 4027
|