Dolphin-Small-Burmese-ASR ( Burmese )

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

Model Overview

• Architecture: Dolphin Small (E-Branchformer Encoder + Transformer Decoder, 372.3M params)
• 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	-	-	95.34	12.85	100.00	53.94	0.00	88.48
Validation Set	250	3.6786	-	31.67	4.36	75.00	6.82	3.18	93.15
Validation Set	500	2.6207	-	25.91	3.77	68.00	4.70	3.48	94.06
Validation Set	750	2.4539	-	27.58	3.92	67.00	4.70	4.09	94.00
Validation Set	1000	1.9528	-	23.64	3.44	62.00	5.15	3.03	95.35
Validation Set	1250	2.0450	-	24.85	3.48	62.00	4.85	3.03	94.19
Validation Set	1500	1.7359	-	21.36	3.04	58.00	3.33	2.73	95.24
Validation Set	1750	1.7104	-	20.45	2.74	56.00	3.48	2.88	95.82
Validation Set	2000	1.6303	-	20.15	2.87	55.00	3.33	2.42	95.64
UNSEEN TEST (Final)	Final	-	-	19.39	3.38	71.24	3.93	2.28	94.57

Usage

import dolphin

# 1. Load fine-tuned model
model = dolphin.load_model("small", 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)