Intended use
- ASR / transcription for Albanian speech (general-purpose).
- Works best on clean speech audio (mono, 16 kHz) with transcripts matching the dataset conventions.
Limitations
- Performance depends heavily on domain/accent/noise conditions; evaluate on your target audio before deployment.
- Streaming / timestamps support is provided by the upstream toolkit; quality of timestamps depends on the forced-aligner setup and audio conditions.
Base model (Qwen3-ASR)
From the upstream model card, Qwen3-ASR provides:
- Language identification + ASR in one model (supports many languages/dialects).
- Offline + streaming inference modes via the
qwen-asr toolkit.
- Optional timestamps via the Qwen forced-aligner.
See the full upstream documentation here:
Install:
Load this model from a local folder (this directory) and transcribe:
import torch
from qwen_asr import Qwen3ASRModel
model = Qwen3ASRModel.from_pretrained(
"./",
dtype=torch.bfloat16,
device_map="cuda:0",
max_inference_batch_size=32,
max_new_tokens=256,
)
results = model.transcribe(
audio="path/to/audio.wav",
language="Albanian",
)
print(results[0].language)
print(results[0].text)
Training data
- Language: Albanian (sq)
- Duration: ~550 hours
- Type: supervised ASR fine-tuning (audio → transcript)
Training recipe (high level)
Extracted from saved transformers training artifacts in this folder:
- Epochs: 2
- Learning rate:
2e-5
- Batch size (per device): 8
- Grad accumulation: 1
- Precision: BF16
- Optimizer:
adamw_torch_fused
- Seed: 42
Notes
- Qwen3-ASR fine-tuning data typically uses transcripts with a language prefix like
language Albanian<asr_text>... (see upstream docs); this fine-tune follows that convention.