---
license: cc
datasets:
- fsicoli/common_voice_16_0
language:
- it
metrics:
- wer 0.0839
---
# Whisper Large v3 Italian

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

This model is a fine-tuned version of [openai/whisper-large-v3] (https://huggingface.co/openai/whisper-large-v3) on Italian using the train and validation split of [Common Voice 16] (https://huggingface.co/datasets/fsicoli/common_voice_16_0).

## Usage

```
from transformers import pipeline

transcriber = pipeline(
  "automatic-speech-recognition", 
  model="fsicoli/whisper-large-v3-it-cv16"
)

transcriber.model.config.forced_decoder_ids = (
  transcriber.tokenizer.get_decoder_prompt_ids(
    language="it", 
    task="transcribe"
  )
)

transcription = transcriber("path/to/my_audio.wav")
```

### Model Description

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

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## Uses

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### Direct 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.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

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

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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).

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## Technical Specifications [optional]

### Model Architecture and Objective

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