Automatic Speech Recognition
Transformers
Lushai
wav2vec2
audio
speech
ctc
mms
low-resource
mizo
Eval Results (legacy)
Instructions to use sulabhkatiyar/ne-asr-lus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sulabhkatiyar/ne-asr-lus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sulabhkatiyar/ne-asr-lus")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("sulabhkatiyar/ne-asr-lus") model = AutoModelForCTC.from_pretrained("sulabhkatiyar/ne-asr-lus", device_map="auto") - Notebooks
- Google Colab
- Kaggle
docs: dataset rename + Vaani source attribution
Browse files
README.md
CHANGED
|
@@ -14,16 +14,16 @@ tags:
|
|
| 14 |
- mizo
|
| 15 |
base_model: facebook/mms-1b-all
|
| 16 |
datasets:
|
| 17 |
-
- sulabhkatiyar/ne-asr-lus
|
| 18 |
-
- sulabhkatiyar/ne-asr-lus-aug
|
| 19 |
model-index:
|
| 20 |
- name: ne-asr-lus
|
| 21 |
results:
|
| 22 |
- task:
|
| 23 |
type: automatic-speech-recognition
|
| 24 |
dataset:
|
| 25 |
-
name: sulabhkatiyar/ne-asr-lus
|
| 26 |
-
type: sulabhkatiyar/ne-asr-lus
|
| 27 |
split: test
|
| 28 |
metrics:
|
| 29 |
- type: wer
|
|
@@ -52,8 +52,10 @@ Output is a Romanized (Latin-script) transcript, lower-cased and NFC-normalized.
|
|
| 52 |
|
| 53 |
## Datasets
|
| 54 |
|
| 55 |
-
- Training / evaluation: [`sulabhkatiyar/ne-asr-lus`](https://huggingface.co/datasets/sulabhkatiyar/ne-asr-lus)
|
| 56 |
-
- Augmented training: [`sulabhkatiyar/ne-asr-lus-aug`](https://huggingface.co/datasets/sulabhkatiyar/ne-asr-lus-aug)
|
|
|
|
|
|
|
| 57 |
|
| 58 |
## Evaluation
|
| 59 |
|
|
@@ -174,3 +176,6 @@ And the NE-ASR adapter release (placeholder; replace when the canonical publicat
|
|
| 174 |
CC-BY-NC-4.0. This adapter is derived from
|
| 175 |
[`facebook/mms-1b-all`](https://huggingface.co/facebook/mms-1b-all), released under
|
| 176 |
CC-BY-NC 4.0. When you use this adapter you must comply with the MMS license terms.
|
|
|
|
|
|
|
|
|
|
|
|
| 14 |
- mizo
|
| 15 |
base_model: facebook/mms-1b-all
|
| 16 |
datasets:
|
| 17 |
+
- sulabhkatiyar/ne-asr-dataset-lus
|
| 18 |
+
- sulabhkatiyar/ne-asr-dataset-lus-aug
|
| 19 |
model-index:
|
| 20 |
- name: ne-asr-lus
|
| 21 |
results:
|
| 22 |
- task:
|
| 23 |
type: automatic-speech-recognition
|
| 24 |
dataset:
|
| 25 |
+
name: sulabhkatiyar/ne-asr-dataset-lus
|
| 26 |
+
type: sulabhkatiyar/ne-asr-dataset-lus
|
| 27 |
split: test
|
| 28 |
metrics:
|
| 29 |
- type: wer
|
|
|
|
| 52 |
|
| 53 |
## Datasets
|
| 54 |
|
| 55 |
+
- Training / evaluation: [`sulabhkatiyar/ne-asr-dataset-lus`](https://huggingface.co/datasets/sulabhkatiyar/ne-asr-dataset-lus)
|
| 56 |
+
- Augmented training: [`sulabhkatiyar/ne-asr-dataset-lus-aug`](https://huggingface.co/datasets/sulabhkatiyar/ne-asr-dataset-lus-aug)
|
| 57 |
+
|
| 58 |
+
**Data source & attribution.** The training and evaluation data is derived from the **ARTPARK-IISc Vaani** project (https://vaani.iisc.ac.in/), released under **CC-BY-4.0**. Please retain that attribution when you use these models.
|
| 59 |
|
| 60 |
## Evaluation
|
| 61 |
|
|
|
|
| 176 |
CC-BY-NC-4.0. This adapter is derived from
|
| 177 |
[`facebook/mms-1b-all`](https://huggingface.co/facebook/mms-1b-all), released under
|
| 178 |
CC-BY-NC 4.0. When you use this adapter you must comply with the MMS license terms.
|
| 179 |
+
The underlying training and evaluation data is derived from the ARTPARK-IISc Vaani
|
| 180 |
+
project (https://vaani.iisc.ac.in/), released under CC-BY-4.0; please also retain that
|
| 181 |
+
attribution.
|