library_name: transformers
tags: []
Model Card for Model ID
Model Details
Model Description
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]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
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
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
Click to expand
```python!pip install transformers datasets torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import torch import torchaudio
model_id = "fastinom/ASR_fassy" 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 audio_file = "/content/drive/MyDrive/recordings/wavefiles/1.wa"#YOUR AUDIO PATH speech = load_audio(audio_file)
inputs = processor(speech, sampling_rate=16000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values).logits predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) print(transcription[0])
</details>
## 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
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]
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[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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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:** [T4 GPU]
- **Hours used:** [3]
- **Cloud Provider:** [Google Colab]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
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#### Software
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## Model Card Authors [optional]
[Fastino Mateteva]
## Model Card Contact
[fastinomateteva@gmail.com]