README.md

---
license: apache-2.0
datasets:
- aisha-org/wav2wec-dataset-100k
language:
- uz
metrics:
- wer
pipeline_tag: automatic-speech-recognition
library_name: transformers
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

```

from transformers import Wav2Vec2CTCTokenizer, SeamlessM4TFeatureExtractor, Wav2Vec2BertProcessor, Wav2Vec2BertForCTC>

# Define the repository name
repo_name = "aisha-org/wev2vec-uzb-100k"

# Load components
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained("./", unk_token="[UNK]", pad_token="[PAD]", word_delimiter_token="|")
feature_extractor = SeamlessM4TFeatureExtractor.from_pretrained(repo_name)
processor = Wav2Vec2BertProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer)

model = Wav2Vec2BertForCTC.from_pretrained(
    repo_name,
    attention_dropout=0.0,
    hidden_dropout=0.0,
    feat_proj_dropout=0.0,
    mask_time_prob=0.0,
    layerdrop=0.0,
    ctc_loss_reduction="mean",
    add_adapter=True,
    pad_token_id=processor.tokenizer.pad_token_id,
    vocab_size=len(processor.tokenizer),
)

# Push components to the Hub
#tokenizer.push_to_hub(repo_name)
#feature_extractor.push_to_hub(repo_name)
#processor.push_to_hub(repo_name)
#model.push_to_hub(repo_name)

# Define the pipeline
pipe = pipeline("automatic-speech-recognition", model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)
# Example usage of the pipeline
def transcribe_audio(file_path):
    result = pipe(file_path)
    return result


```

### Model Description

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- **Developed by:** [More Information Needed]
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- **Model type:** [More Information Needed]
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### Model Sources [optional]

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- **Repository:** [More Information Needed]
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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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