Hugging Face's logo

yonas
/
stt_rw_conformer_ctc_large

Automatic Speech Recognition
NeMo
PyTorch
Kinyarwanda
speech
Kinyarwanda
audio
CTC
Conformer
Transformer
NeMo
Model card Files Community
stt_rw_conformer_ctc_large
File size: 2,717 Bytes 
12fb3ac
 
89adee6
12fb3ac
 
 
 
 
 
 
 
 
 
 
 
019ca69
 
6703bb4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
12fb3ac
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
 
---
language:
- rw
license: cc-by-4.0
library_name: nemo
tags:
- automatic-speech-recognition
- speech
- Kinyarwanda
- audio
- CTC
- Conformer
- Transformer
- NeMo
- pytorch
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: stt_rw_conformer_ctc_large
  results:
  - task:
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: mozilla-foundation/common_voice_11_0
      config: other
      split: test
      args:
        language: rw
    metrics:
    - type: wer
      value: 17.59
      name: WER
-


## Model Overview

<DESCRIBE IN ONE LINE THE MODEL AND ITS USE>

## NVIDIA NeMo: Training

To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
```
pip install nemo_toolkit['all']
``` 

## How to Use this Model

The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

### Automatically instantiate the model

```python
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained("yonas/stt_rw_conformer_ctc_large")
```

### Transcribing using Python
First, let's get a sample
```
wget https://dldata-public.s3.us-east-2.amazonaws.com/2086-149220-0033.wav
```
Then simply do:
```
asr_model.transcribe(['2086-149220-0033.wav'])
```

### Transcribing many audio files

```shell
python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py  pretrained_name="yonas/stt_rw_conformer_ctc_large"  audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
```

### Input

This model accepts 16000 KHz Mono-channel Audio (wav files) as input.

### Output

This model provides transcribed speech as a string for a given audio sample.

## Model Architecture

<ADD SOME INFORMATION ABOUT THE ARCHITECTURE>

## Training

<ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC>

### Datasets

<LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)>

## Performance

<LIST THE SCORES OF THE MODEL - 
      OR
USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS>

## Limitations

<DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL>

Eg: 
Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.


## References

<ADD ANY REFERENCES HERE AS NEEDED>

[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)