

# wav2vec2-xlsr-korean-senior

Futher fine-tuned [fleek/wav2vec-large-xlsr-korean](https://huggingface.co/fleek/wav2vec-large-xlsr-korean) using the [AIhub 자유대화 음성(노인남녀)](https://aihub.or.kr/aidata/30704).

- Total train data size: 808,642
- Total vaild data size: 159,970

When using this model, make sure that your speech input is sampled at 16kHz.

The script used for training can be found here: https://github.com/hyyoka/wav2vec2-korean-senior


### Inference

``` py
import torchaudio
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import re

def clean_up(transcription):
    hangul = re.compile('[^ ㄱ-ㅣ가-힣]+')
    result = hangul.sub('', transcription)
    return result

model_name "hyyoka/wav2vec2-xlsr-korean-senior"
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = Wav2Vec2ForCTC.from_pretrained(model_name)
speech_array, sampling_rate = torchaudio.load(wav_file)
feat = processor(speech_array[0], 
                            sampling_rate=16000, 
                            padding=True,
                            max_length=800000, 
                            truncation=True,
                            return_attention_mask=True,
                            return_tensors="pt",
                            pad_token_id=49
                            )
input = {'input_values': feat['input_values'],'attention_mask':feat['attention_mask']}

outputs = model(**input, output_attentions=True)
logits = outputs.logits
predicted_ids = logits.argmax(axis=-1)
transcription = processor.decode(predicted_ids[0])
stt_result = clean_up(transcription)
```

{"language": "kr", "license": "apache-2.0", "tags": ["automatic-speech-recognition"], "datasets": ["aihub \uc790\uc720\ub300\ud654 \uc74c\uc131(\ub178\uc778\ub0a8\ub140)"]}
transformers pytorch wav2vec2 automatic-speech-recognition kr license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #kr #license-apache-2.0 #endpoints_compatible #region-us 



# wav2vec2-xlsr-korean-senior

Futher fine-tuned fleek/wav2vec-large-xlsr-korean using the AIhub 자유대화 음성(노인남녀).

- Total train data size: 808,642
- Total vaild data size: 159,970

When using this model, make sure that your speech input is sampled at 16kHz.

The script used for training can be found here: URL


### Inference



# wav2vec2-xlsr-korean-senior

Futher fine-tuned fleek/wav2vec-large-xlsr-korean using the AIhub 자유대화 음성(노인남녀).

- Total train data size: 808,642
- Total vaild data size: 159,970

When using this model, make sure that your speech input is sampled at 16kHz.

The script used for training can be found here: URL ### Inference
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #kr #license-apache-2.0 #endpoints_compatible #region-us 
 # wav2vec2-xlsr-korean-senior

Futher fine-tuned fleek/wav2vec-large-xlsr-korean using the AIhub 자유대화 음성(노인남녀).

- Total train data size: 808,642
- Total vaild data size: 159,970

When using this model, make sure that your speech input is sampled at 16kHz.

The script used for training can be found here: URL ### Inference
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #kr #license-apache-2.0 #endpoints_compatible #region-us 
# wav2vec2-xlsr-korean-senior

Futher fine-tuned fleek/wav2vec-large-xlsr-korean using the AIhub 자유대화 음성(노인남녀).

- Total train data size: 808,642
- Total vaild data size: 159,970

When using this model, make sure that your speech input is sampled at 16kHz.

The script used for training can be found here: URL### Inference