Meta-MMS-300M-ASR ( Burmese )

This model is a fine-tuned version of facebook/mms-300m for Automatic Speech Recognition (ASR) in Burmese .

Model Overview

• Architecture: Meta MMS (300M)
• Parameters: ~317M
• Task: Automatic Speech Recognition (ASR)
• Language: Burmese (my)
• Sampling Rate: 16,000 Hz (Mono)
• Tokenizer / Vocab: 64 Burmese Character Tokens (CTC Head)

Dataset Details

The model was trained on a standardized, high-quality Burmese speech corpus:

• Total Duration: ~22 Hours of audio
• Total Utterances: 24,560 WAV files (16 kHz, 16-bit PCM, Mono)
• Total Speakers: 13 Synthetic Burmese Speakers
• Speakers: ပီယ ၊ ဝါစာ ၊ သီရိ ၊ ဒီပ ၊ အက္ခရာ ၊ သဒ္ဒါ ၊ သရ ၊ တာရာ ၊ ကဝိ ၊ သုတ ၊ ပသာဒ ၊ ဂီတ ၊ နန္ဒ
• Training & Validation: 11 speakers (20,699 train utterances, 2,300 validation utterances)
• Test Set (Held-Out): 2 unseen speakers ( ဂီတ & နန္ဒ , 1,561 utterances )
• Text Preprocessing: Standardized Myanmar Unicode with unified word segmentation and punctuation removal. The held-out test split evaluates zero-shot acoustic generalization across completely unseen voices.

Training Configuration

Parameter	Value
Effective Batch Size	64 (8 per device x 4 gradient accumulation)
Base Architecture	facebook/mms-300m ( Wav2Vec 2.0 with 24 Transformer layers, 1024 hidden size, 16 attention heads )
Acoustic Feature Extractor	16 kHz 1D raw waveform input (CNN encoder frozen during training)
Tokenization	Burmese Unicode character CTC vocabulary
Peak Learning Rate	3.0e-4
Learning Rate Scheduler	Linear
Warmup Steps	200
Total Steps	2,000 (~6 Epochs)
Gradient Checkpointing	Enabled (Input Require Grads)
Augmentation	None

Evaluation Results

Stage / Evaluation	Step	Train Loss	Val Loss	WER (%)	CER (%)	SER (%)	DER (%)	IER (%)	chrF
Baseline (Untrained)	0	-	-	100.00	121.74	100.00	84.62	0.00	1.30
Validation Set	250	6.4141	1.0876	67.66	14.87	99.17	10.48	4.22	69.03
Validation Set	500	1.5876	0.4096	37.75	6.34	85.74	6.62	2.86	87.67
Validation Set	750	0.8111	0.2971	28.93	4.44	74.65	3.76	4.08	91.69
Validation Set	1000	0.5157	0.2717	26.27	3.84	70.91	3.76	3.59	93.22
Validation Set	1250	0.3567	0.2459	24.87	3.56	67.22	4.20	2.94	94.06
Validation Set	1500	0.1711	0.2656	23.40	3.25	64.09	2.83	4.11	94.56
Validation Set	1750	0.0564	0.2951	21.23	2.93	60.22	3.20	3.34	95.22
Validation Set	2000	0.0208	0.2972	20.66	2.85	59.35	3.12	3.15	95.39
UNSEEN TEST (Final)	Final	-	-	32.64	6.66	90.01	5.33	2.60	87.22

Usage

import torch
from transformers import pipeline

pipe = pipeline(
    'automatic-speech-recognition',
    model='thantzinphyo/Meta-MMS-300M-ASR',
    device='cuda:0' if torch.cuda.is_available() else 'cpu',
)

output = pipe('audio.wav')
print(output['text'])

References & Citations

• Meta MMS Paper: Scaling Speech Technology to 1,000+ Languages (Pratap et al., 2023)
• Wav2Vec 2.0 Paper: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)