Circassian (Kabardian) ASR Model
This is a fine-tuned model for Automatic Speech Recognition (ASR) in kbd
, based on the facebook/w2v-bert-2.0
model.
The model was trained on a combination of the anzorq/kbd_speech
(filtered on country=russia
) and anzorq/sixuxar_yijiri_mak7
datasets.
Model Details
- Base Model: facebook/w2v-bert-2.0
- Language: Kabardian
- Task: Automatic Speech Recognition (ASR)
- Datasets: anzorq/kbd_speech, anzorq/sixuxar_yijiri_mak7
- Training Steps: 4000
Training
The model was fine-tuned using the following training arguments:
TrainingArguments(
output_dir='output',
group_by_length=True,
per_device_train_batch_size=8,
gradient_accumulation_steps=2,
evaluation_strategy="steps",
num_train_epochs=10,
gradient_checkpointing=True,
fp16=True,
save_steps=1000,
eval_steps=500,
logging_steps=300,
learning_rate=5e-5,
warmup_steps=500,
save_total_limit=2,
push_to_hub=True,
report_to="wandb"
)
Performance
The model's performance during training:
Step | Training Loss | Validation Loss | Wer |
---|---|---|---|
500 | 2.761100 | 0.572304 | 0.830552 |
1000 | 0.325700 | 0.352516 | 0.678261 |
1500 | 0.247000 | 0.271146 | 0.377438 |
2000 | 0.179300 | 0.235156 | 0.319859 |
2500 | 0.176100 | 0.229383 | 0.293537 |
3000 | 0.171600 | 0.208033 | 0.310458 |
3500 | 0.133200 | 0.199517 | 0.289542 |
4000 | 0.117900 | 0.208304 | 0.258989 |
4500 | 0.145400 | 0.184942 | 0.285311 |
5000 | 0.129600 | 0.195167 | 0.372033 |
5500 | 0.122600 | 0.203584 | 0.386369 |
6000 | 0.196800 | 0.270521 | 0.687662 |
Note
To optimize training and reduce tokenizer vocabulary size, prior to training the following digraphs in the training data were replaced with single characters:
гъ -> ɣ
дж -> j
дз -> ӡ
жь -> ʐ
кӏ -> қ
къ -> q
кхъ -> qҳ
лъ -> ɬ
лӏ -> ԯ
пӏ -> ԥ
тӏ -> ҭ
фӏ -> ჶ
хь -> h
хъ -> ҳ
цӏ -> ҵ
щӏ -> ɕ
я -> йа
After obtaining the transcription, reversed replacements can be applied to restore the original characters.
Inference
import torchaudio
from transformers import pipeline
pipe = pipeline(model="anzorq/w2v-bert-2.0-kbd-v2", device=0)
reversed_replacements = {
'ɣ': 'гъ', 'j': 'дж', 'ӡ': 'дз', 'ʐ': 'жь',
'қ': 'кӏ', 'q': 'къ', 'qҳ': 'кхъ', 'ɬ': 'лъ',
'ԯ': 'лӏ', 'ԥ': 'пӏ', 'ҭ': 'тӏ', 'ჶ': 'фӏ',
'h': 'хь', 'ҳ': 'хъ', 'ҵ': 'цӏ', 'ɕ': 'щӏ',
'йа': 'я'
}
def reverse_replace_symbols(text):
for orig, replacement in reversed_replacements.items():
text = text.replace(orig, replacement)
return text
def transcribe_speech(audio_path):
waveform, sample_rate = torchaudio.load(audio_path)
waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)
torchaudio.save("temp.wav", waveform, 16000)
transcription = pipe("temp.wav", chunk_length_s=10)['text']
transcription = reverse_replace_symbols(transcription)
return transcription
audio_path = "audio.wav"
transcription = transcribe_speech(audio_path)
print(f"Transcription: {transcription}")
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