KasuleTrevor

cdli-qwen3-asr-lg-typical-1p7b-base-finetune

Deploy Dedicated

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Get help setting up a custom Dedicated Endpoints.

Talk with our engineer to get a quote for reserved GPU instances with discounts.

Model provider

KasuleTrevor

Model tree

Base

Qwen/Qwen3-ASR-1.7B

Fine-tuned

this model

Modalities

Input

Audio

Output

Text

Pricing

Dedicated Endpoints

Supported Functionality

Model APIs

Dedicated Endpoints

Container

More information

Explore FriendliAI today

Get started Talk to an engineer

KasuleTrevor/cdli-qwen3-asr-lg-typical-1p7b-base-finetune API & Inference Endpoint | FriendliAI

README

License: apache-2.0

Training Setup

Base model: Qwen/Qwen3-ASR-1.7B
Datasets: KasuleTrevor/lg_100hrs + dmusingu/yogera-dataset (Luganda split)
Training language tag: Luganda
Forced inference language: disabled
Epochs: 10
Batch size: 4
Gradient accumulation: 2
Learning rate: 2e-05
Scheduler: cosine
Warmup ratio: 0.03
Save steps: 500
Selected checkpoint: checkpoint-61500
Selection reason: fallback best eval_loss (0.181851)

Final Test Metrics

Corpus WER (normalized): 0.226814
Corpus CER (normalized): 0.066090
Average utterance WER (normalized): 0.230013
Average utterance CER (normalized): 0.067483

Checkpoint Selection Evidence

Table with columns: checkpoint, step, eval_loss
checkpoint	step	eval_loss
checkpoint-61500	61500	0.1818510740995407
checkpoint-62000	62000	0.181895226240158
checkpoint-62500	62500	0.18197962641716
checkpoint-63000	63000	0.1819778531789779

Source Dataset Breakdown

Table with columns: source_dataset, n_samples, mean_wer, mean_cer, median_wer, median_cer
source_dataset	n_samples	mean_wer	mean_cer	median_wer	median_cer
lg100	2828	0.23	0.0675	0.1667	0.0274

Artifacts

Result folder: results/checkpoint-61500/
Includes checkpoint validation summaries, final test predictions, scored outputs, and grouped analyses.