Dolphin-Base-Burmese-ASR ( Burmese )

This model is a fine-tuned version of DataoceanAI/dolphin-base for Automatic Speech Recognition (ASR) in Burmese .

Model Overview

• Architecture: Dolphin Base (E-Branchformer Encoder + Transformer Decoder)
• Parameters: ~140M
• Task: Automatic Speech Recognition (ASR)
• Language: Burmese (my)
• Sampling Rate: 16,000 Hz (Mono)
• Tokenizer / Vocab: Dolphin Multilingual BPE (<my><MM> tokens)

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 (\u1000-\u109F) 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 x 2 GPUs)
Peak Learning Rate	5.0e-5
Learning Rate Scheduler	Cosine
Warmup Steps	200
Total Steps	2,000 (~6 Epochs)
Mixed Precision	FP16
Gradient Checkpointing	Enabled
Augmentation	None

Evaluation Results

Stage / Evaluation	Step	Train Loss	Val Loss	WER (%)	CER (%)	SER (%)	DER (%)	IER (%)	chrF
Baseline (Untrained)	0	-	-	92.13	17.17	100.00	47.23	0.58	78.82
Validation Set	250	5.1032	-	38.18	6.85	83.00	6.52	3.18	88.04
Validation Set	500	3.5427	-	31.67	4.95	74.00	5.15	4.09	92.20
Validation Set	750	2.8435	-	27.88	4.36	72.00	4.39	4.39	93.28
Validation Set	1000	2.4685	-	25.91	3.79	63.00	4.09	3.94	94.30
Validation Set	1250	2.4317	-	25.76	3.85	64.00	5.00	3.48	94.17
Validation Set	1500	2.1806	-	23.48	3.28	62.00	3.48	3.64	95.01
Validation Set	1750	2.0335	-	22.73	3.00	62.00	4.24	3.64	95.45
Validation Set	2000	1.9487	-	22.88	3.00	62.00	3.94	3.79	95.54
UNSEEN TEST (Final)	Final	-	-	23.51	4.42	78.54	4.48	2.43	92.54

Usage

import dolphin

# 1. Load fine-tuned model
model = dolphin.load_model("base", model_dir="path_to_model", device="cuda")

# 2. Transcribe
result = dolphin.transcribe(model, "audio.wav", lang_sym="my", region_sym="MM")
print(result.text_nospecial)