Update README.md

README.md

@@ -1,3 +1,83 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- uz
+- en
+- ru
+metrics:
+- wer
+base_model:
+- openai/whisper-small
+pipeline_tag: automatic-speech-recognition
+tags:
+- speech-recognition
+- whisper
+- multilingual
+- uzbek
+- russian
+- english
+---
+
+# Multilingual Whisper (Uz/En/Ru) — Fine-tuned Speech-to-Text Model
+
+A fine-tuned **Whisper Small** model optimized to transcribe **Uzbek, English, and Russian equally well**.  
+This model is intended for real-world speech transcription with a balanced multilingual dataset and performs competitively against strong open-source and commercial STT solutions.
+
+---
+
+## Model Details
+
+### Model Description
+
+This model extends **OpenAI Whisper Small** by fine-tuning it on a multilingual speech mixture, aimed to deliver robust ASR performance for **Uzbek**, **English**, and **Russian** speakers.  
+The goal was to reduce the performance gap between languages, especially improving **Uzbek** speech recognition, where public ASR resources are scarce.
+
+- **Model type:** Automatic Speech Recognition (ASR)
+- **Language(s):** Uzbek 🇺🇿, English 🇬🇧, Russian 🇷🇺
+- **License:** Apache-2.0
+- **Finetuned from:** openai/whisper-small
+- **Intended usage:** Real-time & offline speech-to-text
+
+---
+## Trained datasets:
+- DavronSherbaev/uzbekvoice-filtered
+- telegram-voice-messages (private collection)
+- navaistt-open-datasets
+- sovaai/russian-audiobooks
+- librispeech
+
+## Evaluation
+
+### Word Error Rate (WER) Comparison
+
+| Model                          | WER ↓    |
+|--------------------------------|----------|
+| Whisper-small-uz-v1  | **34.00%** |
+| Gemini (Commercial)            | 36.21%  |
+| NavaiSTT v2 (Open-Source)     | 35.14%  |
+| Aisha STT (Commercial)         | 41.71%  |
+
+The model **outperforms both commercial and open-source Uzbek STT models**, showing strong generalization for informal real-world speech.
+
+---
+
+## Usage Example
+
+```python
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+import torch
+import torchaudio
+
+model_id = "Firdavs222/whisper-small-uz-v1" # replace with real model repo
+
+processor = WhisperProcessor.from_pretrained(model_id)
+model = WhisperForConditionalGeneration.from_pretrained(model_id)
+
+audio, sr = torchaudio.load("audio.wav")
+inputs = processor(audio, sampling_rate=sr, return_tensors="pt")
+
+with torch.no_grad():
+    predicted_ids = model.generate(inputs.input_features)
+text = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+
+print(text)  # → transcribed text here