pages/Workflow & Model Overview.py

import streamlit as st

# Custom CSS for better styling
st.markdown("""

    <style>

        .main-title {

            font-size: 36px;

            color: #4A90E2;

            font-weight: bold;

            text-align: center;

        }

        .sub-title {

            font-size: 24px;

            color: #4A90E2;

            margin-top: 20px;

        }

        .section {

            background-color: #f9f9f9;

            padding: 15px;

            border-radius: 10px;

            margin-top: 20px;

        }

        .section p, .section ul {

            color: #666666;

        }

        .link {

            color: #4A90E2;

            text-decoration: none;

        }

        .benchmark-table {

            width: 100%;

            border-collapse: collapse;

            margin-top: 20px;

        }

        .benchmark-table th, .benchmark-table td {

            border: 1px solid #ddd;

            padding: 8px;

            text-align: left;

        }

        .benchmark-table th {

            background-color: #4A90E2;

            color: white;

        }

        .benchmark-table td {

            background-color: #f2f2f2;

        }

    </style>

""", unsafe_allow_html=True)

# Main Title
st.markdown('<div class="main-title">Wav2Vec2 for Speech Recognition</div>', unsafe_allow_html=True)

# Description
st.markdown("""

<div class="section">

    <p><strong>Wav2Vec2</strong> is a groundbreaking model in Automatic Speech Recognition (ASR), developed to learn speech representations from raw audio. This model achieves exceptional accuracy with minimal labeled data, making it ideal for low-resource settings. Adapted for Spark NLP, Wav2Vec2 enables scalable, production-ready ASR applications.</p>

</div>

""", unsafe_allow_html=True)

# Why, Where, and When to Use Wav2Vec2
st.markdown('<div class="sub-title">Why, Where, and When to Use Wav2Vec2</div>', unsafe_allow_html=True)
st.markdown("""

<div class="section">

    <p>Use <strong>Wav2Vec2</strong> when you need a robust ASR solution that excels in scenarios with limited labeled data. It’s perfect for various speech-to-text applications where scalability and accuracy are critical. Some ideal use cases include:</p>

    <ul>

        <li><strong>Transcription Services:</strong> Efficiently convert large volumes of speech into text, vital for media, legal, and healthcare industries.</li>

        <li><strong>Voice-Activated Assistants:</strong> Enhance the accuracy of voice commands in smart devices and personal assistants.</li>

        <li><strong>Meeting Summarization:</strong> Automatically transcribe and summarize meetings, aiding in easy content review and catch-up for absentees.</li>

        <li><strong>Language Learning Tools:</strong> Assist learners in improving pronunciation by providing real-time speech-to-text feedback.</li>

        <li><strong>Accessibility Enhancements:</strong> Generate real-time captions for videos and live events, making content accessible to the hearing impaired.</li>

        <li><strong>Call Center Analytics:</strong> Analyze customer interactions for insights and quality monitoring.</li>

    </ul>

</div>

""", unsafe_allow_html=True)

# How to Use the Model
st.markdown('<div class="sub-title">How to Use the Model</div>', unsafe_allow_html=True)
st.code('''

audio_assembler = AudioAssembler() \\

    .setInputCol("audio_content") \\

    .setOutputCol("audio_assembler")



speech_to_text = Wav2Vec2ForCTC \\

    .pretrained("asr_wav2vec2_large_xlsr_53_english_by_jonatasgrosman", "en")\\

    .setInputCols("audio_assembler") \\

    .setOutputCol("text")



pipeline = Pipeline(stages=[

  audio_assembler,

  speech_to_text,

])



pipelineModel = pipeline.fit(audioDf)



pipelineDF = pipelineModel.transform(audioDf)

''', language='python')

# Best Practices & Tips
st.markdown('<div class="sub-title">Best Practices & Tips</div>', unsafe_allow_html=True)
st.markdown("""

<div class="section">

    <ul>

        <li><strong>Preprocessing:</strong> Ensure your audio data is clear and well-prepared by removing background noise and normalizing audio levels for the best transcription results.</li>

        <li><strong>Fine-tuning:</strong> For specific use cases or languages, consider fine-tuning the model on your own dataset to improve accuracy.</li>

        <li><strong>Batch Processing:</strong> Leverage Spark NLP's distributed processing capabilities to handle large-scale audio datasets efficiently.</li>

        <li><strong>Model Evaluation:</strong> Regularly evaluate the model's performance on your specific use case using metrics like Word Error Rate (WER) to ensure it meets your accuracy requirements.</li>

        <li><strong>Resource Management:</strong> When deploying in production, monitor resource usage, especially for large models, to optimize performance and cost.</li>

    </ul>

</div>

""", unsafe_allow_html=True)

# Model Information
st.markdown('<div class="sub-title">Model Information</div>', unsafe_allow_html=True)
st.markdown("""

<div class="section">

    <table class="benchmark-table">

        <tr>

            <th>Attribute</th>

            <th>Description</th>

        </tr>

        <tr>

            <td><strong>Model Name</strong></td>

            <td>asr_wav2vec2_large_xlsr_53_english_by_jonatasgrosman</td>

        </tr>

        <tr>

            <td><strong>Compatibility</strong></td>

            <td>Spark NLP 4.2.0+</td>

        </tr>

        <tr>

            <td><strong>License</strong></td>

            <td>Open Source</td>

        </tr>

        <tr>

            <td><strong>Edition</strong></td>

            <td>Official</td>

        </tr>

        <tr>

            <td><strong>Input Labels</strong></td>

            <td>[audio_assembler]</td>

        </tr>

        <tr>

            <td><strong>Output Labels</strong></td>

            <td>[text]</td>

        </tr>

        <tr>

            <td><strong>Language</strong></td>

            <td>en</td>

        </tr>

        <tr>

            <td><strong>Size</strong></td>

            <td>1.2 GB</td>

        </tr>

    </table>

</div>

""", unsafe_allow_html=True)

# Data Source Section
st.markdown('<div class="sub-title">Data Source</div>', unsafe_allow_html=True)
st.markdown("""

<div class="section">

    <p>The Wav2Vec2 model is available on <a class="link" href="https://huggingface.co/jonatasgrosman/asr_wav2vec2_large_xlsr_53_english" target="_blank">Hugging Face</a>. This model, trained by <em>jonatasgrosman</em>, has been adapted for use with Spark NLP, ensuring it is optimized for large-scale applications.</p>

</div>

""", unsafe_allow_html=True)

# Conclusion
st.markdown('<div class="sub-title">Conclusion</div>', unsafe_allow_html=True)
st.markdown("""

<div class="section">

    <p><strong>Wav2Vec2</strong> is a versatile and powerful ASR model that excels in scenarios with limited labeled data, making it a game-changer in the field of speech recognition. Its seamless integration with Spark NLP allows for scalable, efficient, and accurate deployment in various real-world applications, from transcription services to voice-activated systems.</p>

</div>

""", unsafe_allow_html=True)

# References
st.markdown('<div class="sub-title">References</div>', unsafe_allow_html=True)
st.markdown("""

<div class="section">

    <ul>

        <li><a class="link" href="https://sparknlp.org/2022/09/24/asr_wav2vec2_large_xlsr_53_english_by_jonatasgrosman_en.html" target="_blank">Wav2Vec2 Model on Spark NLP</a></li>

        <li><a class="link" href="https://huggingface.co/jonatasgrosman/asr_wav2vec2_large_xlsr_53_english" target="_blank">Wav2Vec2 Model on Hugging Face</a></li>

        <li><a class="link" href="https://arxiv.org/abs/2006.11477" target="_blank">wav2vec 2.0 Paper</a></li>

        <li><a class="link" href="https://github.com/pytorch/fairseq/tree/master/examples/wav2vec" target="_blank">Wav2Vec2 GitHub Repository</a></li>

    </ul>

</div>

""", unsafe_allow_html=True)

# Community & Support
st.markdown('<div class="sub-title">Community & Support</div>', unsafe_allow_html=True)
st.markdown("""

<div class="section">

    <ul>

        <li><a class="link" href="https://sparknlp.org/" target="_blank">Official Website</a>: Comprehensive documentation and examples.</li>

        <li><a class="link" href="https://join.slack.com/t/spark-nlp/shared_invite/zt-198dipu77-L3UWNe_AJ8xqDk0ivmih5Q" target="_blank">Slack</a>: Join the community for live discussions and support.</li>

        <li><a class="link" href="https://github.com/JohnSnowLabs/spark-nlp" target="_blank">GitHub</a>: Report issues, request features, and contribute to the project.</li>

        <li><a class="link" href="https://medium.com/spark-nlp" target="_blank">Medium</a>: Read articles and tutorials about Spark NLP.</li>

        <li><a class="link" href="https://www.youtube.com/channel/UCmFOjlpYEhxf_wJUDuz6xxQ/videos" target="_blank">YouTube</a>: Watch video tutorials and demonstrations.</li>

    </ul>

</div>

""", unsafe_allow_html=True)