Create README.md

README.md

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+---
+license: mit
+language:
+- pt
+base_model:
+- distil-whisper/distil-large-v3
+pipeline_tag: automatic-speech-recognition
+tags:
+- asr
+- pt
+- ptbr
+- stt
+- speech-to-text
+- automatic-speech-recognition
+---
+# Distil-Whisper-Large-v3 for Brazilian Portuguese
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model is a fine-tuned version of distil-whisper-large-v3 for automatic speech recognition (ASR) in Brazilian Portuguese. It was trained using the Common Voice 16 dataset in conjunction with a private dataset transcribed using Whisper Large v3.
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+The model aims to perform automatic speech transcription in Brazilian Portuguese with high accuracy. By combining data from Common Voice 16 with an automatically transcribed private dataset, the model achieved a Word Error Rate (WER) of 8.93% on the validation set of Common Voice 16.
+
+- **Model type:** Speech recognition model based on distil-whisper-large-v3
+- **Language(s) (NLP):** Brazilian Portuguese (pt-BR)
+- **License:** MIT
+- **Finetuned from model [optional]:** distil-whisper/distil-large-v3
+
+## How to Get Started with the Model
+
+You can use the model with the Transformers library:
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+
+```python   
+processor = WhisperProcessor.from_pretrained("freds0/distil-whisper-large-v3-ptbr")
+model = WhisperForConditionalGeneration.from_pretrained("freds0/distil-whisper-large-v3-ptbr")
+
+# Load audio and process
+audio_input = ...  # your audio here
+input_features = processor(audio_input, sampling_rate=16000, return_tensors="pt").input_features
+
+# Generate transcription
+predicted_ids = model.generate(input_features)
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+print(transcription[0])
+```
+
+