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--- |
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license: apache-2.0 |
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pipeline_tag: audio-text-to-text |
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library_name: transformers |
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--- |
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# felguk-omni-v0: Audio-to-Text Conversion Model |
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**Model Name:** felguk-omni-v0 |
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**Type:** Audio-to-Text |
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**Download Method:** Nexa-SDK |
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## Overview |
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The `felguk-omni-v0` model is designed to convert audio inputs into text transcriptions with high accuracy. It leverages advanced deep learning techniques to understand and process spoken language across various domains and languages. This model is ideal for applications such as automatic speech recognition (ASR), transcription services, and voice command interfaces. |
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## Features |
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- **High Accuracy:** State-of-the-art performance in converting audio to text. |
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- **Multilingual Support:** Capable of recognizing multiple languages. |
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- **Real-Time Processing:** Optimized for low-latency transcription. |
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- **Easy Integration:** Simple API access through Nexa-SDK. |
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## Installation |
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Before using the `felguk-omni-v0` model, ensure you have the Nexa-SDK installed. Follow the instructions below to set up your environment: |
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### Prerequisites |
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- Python 3.7 or later |
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- Internet connection |
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- Hugging Face account (optional but recommended) |
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### Installing Nexa-SDK |
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You can install the Nexa-SDK via pip: |
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```bash |
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pip install nexa-sdk |
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``` |
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## Downloading the Model |
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To download the felguk-omni-v0 model using Nexa-SDK, run the following command: |
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```bash |
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from nexa_sdk import ModelDownloader |
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# Initialize the downloader |
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downloader = ModelDownloader() |
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# Download the model |
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model = downloader.download_model("felguk-omni-v0") |
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print("Model downloaded successfully!") |
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``` |
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### Usage Example |
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Here’s a simple example of how to use the felguk-omni-v0 model to transcribe an audio file: |
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```bash |
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from nexa_sdk import ModelLoader |
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# Load the model |
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model = ModelLoader.load("felguk-omni-v0") |
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# Path to your audio file |
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audio_file_path = "path/to/your/audio.wav" |
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# Transcribe the audio |
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transcription = model.transcribe(audio_file_path) |
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print(f"Transcription: {transcription}") |
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``` |
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## Model Performance |
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The `felguk-omni-v0` model has been rigorously tested and demonstrates exceptional performance across various Automatic Speech Recognition (ASR) benchmarks. Here are some of the key performance metrics: |
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| Metric | Value | |
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| Word Error Rate (WER) | < 5% | |
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| Language Support | English, Spanish, French, German, etc. | |
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| Latency | ~200ms per second of audio | |
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| Vocabulary Size | 60,000+ words | |
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| Supported Audio Formats | WAV, MP3, FLAC | |
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| Average Processing Time | 1.2x real-time | |
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## Acknowledgements |
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Special thanks to the developers and contributors who made this model possible. We also extend our gratitude to the Hugging Face team for providing the platform to host and share this model. Additionally, we appreciate the support and feedback from our user community, which has been invaluable in refining and improving the `felguk-omni-v0` model. |
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For further assistance, please visit the [Hugging Face forums](https://discuss.huggingface.co/) or contact us at [email protected]. |
