README.md

---
library_name: transformers
tags: []
---

# Model Card for Model ID

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



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. 

- **Developed by:** [Fastino Mateteva]
- **Model type:** [Transformer model]
- **Language(s) (NLP):** [Shona]
- **License:** []


### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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.

[### Running the model 

<details>
<summary> Click to expand </summary>
```python

!pip install transformers datasets torchaudio

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
import torchaudio

model_id = "fastinom/ASR_fassy"
# Load model and processor
model = Wav2Vec2ForCTC.from_pretrained(model_id)
processor = Wav2Vec2Processor.from_pretrained(model_id)

def load_audio(file_path):
    speech_array, sampling_rate = torchaudio.load(file_path)
    resampler = torchaudio.transforms.Resample(sampling_rate, 16000)
    speech = resampler(speech_array).squeeze().numpy()
    return speech
# Example audio file path
audio_file = "/content/drive/MyDrive/recordings/wavefiles/1.wa"#YOUR AUDIO PATH
speech = load_audio(audio_file)

# Preprocess the audio
inputs = processor(speech, sampling_rate=16000, return_tensors="pt", padding=True)

# Perform inference
with torch.no_grad():
    logits = model(inputs.input_values).logits

# Decode the output
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
print(transcription[0])
```

</details>]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### 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]

[More Information Needed]

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-4
- per_device_train_batch_size=4
- eval_batch_size: 2
- evaluation_strategy="steps"
- gradient_checkpointing=True
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- num_train_epochs=3
- save_total_limit=1
- fp16=True
- save_steps=400
- eval_steps=200
- logging_steps=200
- push_to_hub=True

### Training results

| Training Loss | WER   | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.427         | 1.00  | 200   | 4.1518          |
| 3.7979        | 1.00  | 400   | 3.8410          |
| 3.6924        | 1.00  | 600   | 3.4249          |
| 0.8357        | 0.26  | 800   | 0.2396          |
| 0.1528        | 0.24  | 1000  | 0.2155          |
| 0.1415        | 0.24  | 1200  | 0.2036          |
| 0.1278        | 0.24  | 1400  | 0.2028          |



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

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]


## 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:** [T4 GPU]
- **Hours used:** [3]
- **Cloud Provider:** [Google Colab]


## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]


## Model Card Authors [optional]

[Fastino Mateteva]

## Model Card Contact

[fastinomateteva@gmail.com]