# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("WindyWord/listen-windy-lingua-ps", device_map="auto")WindyWord.ai STT — Pashto Lingua (GPU (safetensors))
Part of the Windstorm Labs open model catalogue. Scores, licence and attribution for every model: https://windytranslate.com/models/listen-windy-lingua-ps
Transcribes Pashto speech (Indo-European > Indo-Iranian > Iranian).
Note: EXCELLENT tier when used correctly. Derived from
ihanif/whisper-medium-pashto. Verified at WER 5.3% / CER 3.2% / script-match 99.2% on 50-sample FLEURS ps_af when inference usesforced_decoder_ids(passed explicitly tomodel.generate()viaprocessor.get_decoder_prompt_ids(language='pashto', task='transcribe')). With the conveniencelanguage=kwarg the model can silently drop the Pashto token and hallucinate English script on ~30% of samples (53.7% WER artifact). Always force the decoder prompt for Pashto inference.
Quality
- WER: not yet measured by us. Imported from an upstream community fine-tune.
About this variant
This is the safetensors deployment format of our Pashto Lingua STT model. Load it via the safetensors/ subfolder.
Part of the WindyWord.ai STT fleet — covering 35+ languages that commercial speech-to-text APIs underserve, with proper dialect / script disclosures where they matter.
Usage
from transformers import WhisperForConditionalGeneration, WhisperProcessor
processor = WhisperProcessor.from_pretrained("WindyWord/listen-windy-lingua-ps", subfolder="safetensors")
model = WhisperForConditionalGeneration.from_pretrained("WindyWord/listen-windy-lingua-ps", subfolder="safetensors")
Apps
The Windy Word apps are built on this model family.
Provenance & License
Weights derived from ihanif/whisper-medium-pashto (community fine-tune by ihanif; identified from this repository's commit history and config.json). Licensed Apache-2.0 upstream; this redistribution is released under the same licence.
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="WindyWord/listen-windy-lingua-ps")