Qipchoq STT
Qipchoq-STT is a fine-tuned automatic speech recognition (ASR) model based on xmullaboyeva/mullaboyeva-stt. The model converts speech audio into text and is intended for research and practical speech recognition...
Fuente del modelo
- Tipo
- Text model
- Tamaño del archivo
- 2,85 GB
- Modelo base
- xmullaboyeva/mullaboyeva-stt
- Versión
- vmodel
- Actualizado
- 26 oct 2026
- Descargas
- 4
- Fuente
- Hugging Face
Descripción de la fuente
Qipchoq-STT
Qipchoq-STT is a fine-tuned automatic speech recognition (ASR) model based on xmullaboyeva/mullaboyeva-stt. The model converts speech audio into text and is intended for research and practical speech recognition applications.
Model description
This model was fine-tuned from xmullaboyeva/mullaboyeva-stt using a custom speech dataset. It is designed for speech-to-text transcription and can be integrated into Python applications using the Hugging Face Transformers library.
Usage
from transformers import pipeline
pipe = pipeline(
"automatic-speech-recognition",
model="xmullaboyeva/Qipchoq-STT"
)
result = pipe("audio.wav")
print(result["text"])
Intended uses
The model can be used for:
- Automatic speech recognition (ASR)
- Speech-to-text transcription
- Speech technology research
- Voice-enabled applications
- Dataset transcription and annotation
Limitations
Recognition accuracy may decrease for:
- Noisy audio recordings
- Speech with accents or dialects not represented in the training data
- Overlapping speakers
- Extremely short audio clips
Training and evaluation data
The model was fine-tuned on a custom speech dataset.
Training procedure
Training hyperparameters
| Hyperparameter | Value |
|---|---|
| Learning rate | 1e-5 |
| Train batch size | 8 |
| Evaluation batch size | 2 |
| Gradient accumulation steps | 4 |
| Total train batch size | 32 |
| Optimizer | Adam (β1=0.9, β2=0.999, ε=1e-8) |
| Learning rate scheduler | Cosine |
| Warmup ratio | 0.05 |
| Number of epochs | 5 |
| Random seed | 42 |
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2231 | 1.97 | 200 | 0.1962 |
| 0.1199 | 3.93 | 400 | 0.2005 |
Final validation loss: 0.2005
Framework versions
- Transformers 4.38.0
- PyTorch 2.9.1+cu126
- Datasets 2.18.0
- Tokenizers 0.15.2
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 26 oct
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- license: apache-2.0 base_model: xmullaboyeva/mullaboyeva-stt tags: - automatic-speech-recognition - speech - asr - uzbek - generated_from_trainer model-index: - name: Qipchoq-STT results: [] --- # Qipchoq-STT Qipchoq-STT is a fine-tuned automatic speech recognition (ASR) model based on **xmullaboyeva/mullaboyeva-stt**. The model converts speech audio into text and is intended for research and practical speech recognition applications. ## Model description This model was fine-tuned from **xmullaboyeva/mullaboyeva-stt** using a custom speech dataset. It is designed for speech-to-text transcription and can be integrated into Python applications using the Hugging Face Transformers library. ## Usage ```python from transformers import pipeline pipe = pipeline( "automatic-speech-recognition", model="xmullaboyeva/Qipchoq-STT" ) result = pipe("audio.wav") print(result["text"]) ``` ## Intended uses The model can be used for: - Automatic speech recognition (ASR) - Speech-to-text transcription - Speech technology research - Voice-enabled applications - Dataset transcription and annotation ## Limitations Recognition accuracy may decrease for: - Noisy audio recordings - Speech with accents or dialects not represented in the training data - Overlapping speakers - Extremely short audio clips ## Training and evaluation data The model was fine-tuned on a custom speech dataset. ## Training procedure ### Training hyperparameters | Hyperparameter | Value | |---------------|-------| | Learning rate | 1e-5 | | Train batch size | 8 | | Evaluation batch size | 2 | | Gradient accumulation steps | 4 | | Total train batch size | 32 | | Optimizer | Adam (β₁=0.9, β₂=0.999, ε=1e-8) | | Learning rate scheduler | Cosine | | Warmup ratio | 0.05 | | Number of epochs | 5 | | Random seed | 42 | ### Training results | Training Loss | Epoch | Step | Validation Loss | |--------------:|------:|-----:|----------------:| | 0.2231 | 1.97 | 200 | 0.1962 | | 0.1199 | 3.93 | 400 | 0.2005 | **Final validation loss:** **0.2005** ## Framework versions - Transformers 4.38.0 - PyTorch 2.9.1+cu126 - Datasets 2.18.0 - Tokenizers 0.15.2
Source context: 4 downloads · 0 likes · Pipeline automatic-speech-recognition · Repo xmullaboyeva/Qipchoq-STT
Source context: 35 downloads · 0 likes · Pipeline automatic-speech-recognition · Repo xmullaboyeva/Qipchoq-STT
