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.streamlit/config.toml

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+[theme]
+base="light"
+primaryColor="#29B4E8"

Demo.py

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+import streamlit as st
+import sparknlp
+import os
+import pandas as pd
+import librosa
+
+from sparknlp.base import *
+from sparknlp.common import *
+from sparknlp.annotator import *
+from pyspark.ml import Pipeline
+from sparknlp.pretrained import PretrainedPipeline
+from pyspark.sql.types import *
+import pyspark.sql.functions as F
+
+# Page configuration
+st.set_page_config(
+    layout="wide", 
+    initial_sidebar_state="auto"
+)
+
+# Custom CSS for styling
+st.markdown("""
+    <style>
+        .main-title {
+            font-size: 36px;
+            color: #4A90E2;
+            font-weight: bold;
+            text-align: center;
+        }
+        .section {
+            background-color: #f9f9f9;
+            padding: 10px;
+            border-radius: 10px;
+            margin-top: 10px;
+        }
+        .section p, .section ul {
+            color: #666666;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+@st.cache_resource
+def init_spark():
+    """Initialize Spark NLP."""
+    return sparknlp.start()
+
+@st.cache_resource
+def create_pipeline(model):
+    """Create a Spark NLP pipeline for audio processing."""
+    audio_assembler = AudioAssembler() \
+        .setInputCol("audio_content") \
+        .setOutputCol("audio_assembler")
+
+    speech_to_text = WhisperForCTC \
+        .pretrained(model)\
+        .setInputCols("audio_assembler") \
+        .setOutputCol("text")
+
+    pipeline = Pipeline(stages=[
+        audio_assembler,
+        speech_to_text
+    ])
+    return pipeline
+
+def fit_data(pipeline, fed_data):
+    """Fit the data into the pipeline and return the transcription."""
+    data, sampling_rate = librosa.load(fed_data, sr=16000)
+    data = data.tolist()
+    spark_df = spark.createDataFrame([[data]], ["audio_content"])
+
+    model = pipeline.fit(spark_df)
+    lp = LightPipeline(model)
+    lp_result = lp.fullAnnotate(data)[0]
+    return lp_result
+
+def save_uploadedfile(uploadedfile, path):
+    """Save the uploaded file to the specified path."""
+    filepath = os.path.join(path, uploadedfile.name)
+    with open(filepath, "wb") as f:
+        if hasattr(uploadedfile, 'getbuffer'):
+            f.write(uploadedfile.getbuffer())
+        else:
+            f.write(uploadedfile.read())
+
+# Sidebar content
+model_list = ["asr_whisper_small_english"]
+model = st.sidebar.selectbox(
+    "Choose the pretrained model",
+    model_list,
+    help="For more info about the models visit: https://sparknlp.org/models"
+)
+
+# Main content
+st.markdown('<div class="main-title">Speech Recognition With WhisperForCTC</div>', unsafe_allow_html=True)
+st.markdown('<div class="section"><p>This demo transcribes audio files into texts using the <code>WhisperForCTC</code> Annotator and advanced speech recognition models.</p></div>', unsafe_allow_html=True)
+
+# Reference notebook link in sidebar
+st.sidebar.markdown('Reference notebook:')
+st.sidebar.markdown("""
+    <a href="https://github.com/JohnSnowLabs/spark-nlp/blob/master/examples/python/annotation/audio/whisper/Automatic_Speech_Recognition_Whisper_(WhisperForCTC).ipynb">
+        <img src="https://colab.research.google.com/assets/colab-badge.svg" style="zoom: 1.3" alt="Open In Colab"/>
+    </a>
+""", unsafe_allow_html=True)
+
+# Load examples
+AUDIO_FILE_PATH = "inputs"
+audio_files = sorted(os.listdir(AUDIO_FILE_PATH))
+
+selected_audio = st.selectbox("Select an audio", audio_files)
+
+# Creating a simplified Python list of audio file types
+audio_file_types = ["mp3", "flac", "wav", "aac", "ogg", "aiff", "wma", "m4a", "ape", "dsf", "dff", "midi", "mid", "opus", "amr"]
+uploadedfile = st.file_uploader("Try it for yourself!", type=audio_file_types)
+
+if uploadedfile:
+    selected_audio = f"{AUDIO_FILE_PATH}/{uploadedfile.name}"
+    save_uploadedfile(uploadedfile, AUDIO_FILE_PATH)
+elif selected_audio:
+    selected_audio = f"{AUDIO_FILE_PATH}/{selected_audio}"
+
+# Audio playback and transcription
+st.subheader("Play Audio")
+
+with open(selected_audio, 'rb') as audio_file:
+    audio_bytes = audio_file.read()
+st.audio(audio_bytes)
+
+spark = init_spark()
+pipeline = create_pipeline(model)
+output = fit_data(pipeline, selected_audio)
+
+st.subheader(f"Transcription:")
+st.markdown(f"{(output['text'][0].result).title()}")

Dockerfile

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+# Download base image ubuntu 18.04
+FROM ubuntu:18.04
+
+# Set environment variables
+ENV NB_USER jovyan
+ENV NB_UID 1000
+ENV HOME /home/${NB_USER}
+
+# Install required packages
+RUN apt-get update && apt-get install -y \
+    tar \
+    wget \
+    bash \
+    rsync \
+    gcc \
+    libfreetype6-dev \
+    libhdf5-serial-dev \
+    libpng-dev \
+    libzmq3-dev \
+    python3 \
+    python3-dev \
+    python3-pip \
+    unzip \
+    pkg-config \
+    software-properties-common \
+    graphviz \
+    openjdk-8-jdk \
+    ant \
+    ca-certificates-java \
+    && apt-get clean \
+    && update-ca-certificates -f;
+
+# Install Python 3.8 and pip
+RUN add-apt-repository ppa:deadsnakes/ppa \
+    && apt-get update \
+    && apt-get install -y python3.8 python3-pip \
+    && apt-get clean;
+
+# Set up JAVA_HOME
+ENV JAVA_HOME /usr/lib/jvm/java-8-openjdk-amd64/
+RUN mkdir -p ${HOME} \
+    && echo "export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/" >> ${HOME}/.bashrc \
+    && chown -R ${NB_UID}:${NB_UID} ${HOME}
+
+# Create a new user named "jovyan" with user ID 1000
+RUN useradd -m -u ${NB_UID} ${NB_USER}
+
+# Switch to the "jovyan" user
+USER ${NB_USER}
+
+# Set home and path variables for the user
+ENV HOME=/home/${NB_USER} \
+    PATH=/home/${NB_USER}/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR ${HOME}
+
+# Upgrade pip and install Python dependencies
+RUN python3.8 -m pip install --upgrade pip
+COPY requirements.txt /tmp/requirements.txt
+RUN python3.8 -m pip install -r /tmp/requirements.txt
+
+# Copy the application code into the container at /home/jovyan
+COPY --chown=${NB_USER}:${NB_USER} . ${HOME}
+
+# Expose port for Streamlit
+EXPOSE 7860
+
+# Define the entry point for the container
+ENTRYPOINT ["streamlit", "run", "Demo.py", "--server.port=7860", "--server.address=0.0.0.0"]

pages/Workflow & Model Overview.py

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+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">Whisper: Advanced Speech Recognition</div>', unsafe_allow_html=True)
+
+# Overview Section
+st.markdown("""
+<div class="section">
+    <p>The <strong>Whisper</strong> model, developed by OpenAI, was introduced in the paper <em>Robust Speech Recognition via Large-Scale Weak Supervision</em>. Whisper is a cutting-edge speech recognition model designed to handle a wide range of tasks by learning from an extensive dataset of 680,000 hours of multilingual and multitask audio transcripts.</p>
+    <p>Whisper's robust architecture allows it to perform well across different speech processing tasks without the need for fine-tuning. Its zero-shot transfer capabilities enable it to generalize effectively, making it a versatile tool for developers and researchers alike.</p>
+</div>
+""", unsafe_allow_html=True)
+
+# Use Cases Section
+st.markdown('<div class="sub-title">Use Cases</div>', unsafe_allow_html=True)
+st.markdown("""
+<div class="section">
+    <ul>
+        <li><strong>Transcription Services:</strong> Automate transcription of audio files in English for media, legal, and academic purposes.</li>
+        <li><strong>Voice-Activated Assistants:</strong> Enhance voice command recognition in smart devices and applications.</li>
+        <li><strong>Broadcast Media:</strong> Provide real-time transcription and subtitling for live broadcasts.</li>
+        <li><strong>Multilingual Translation:</strong> Use as a base for developing multilingual speech-to-text and translation services.</li>
+    </ul>
+</div>
+""", unsafe_allow_html=True)
+
+# How to Use Section
+st.markdown('<div class="sub-title">How to Use Whisper</div>', unsafe_allow_html=True)
+st.code('''
+audioAssembler = AudioAssembler() \\
+    .setInputCol("audio_content") \\
+    .setOutputCol("audio_assembler")
+
+speechToText = WhisperForCTC \\
+    .pretrained("asr_whisper_small_english")\\
+    .setInputCols("audio_assembler") \\
+    .setOutputCol("text")
+
+pipeline = Pipeline().setStages([audioAssembler, speechToText])
+
+pipelineModel = pipeline.fit(data)
+
+pipelineDF = pipelineModel.transform(data)
+''', language='python')
+
+st.markdown("""
+<div class="section">
+    <p>This example demonstrates how to use Whisper in a Spark NLP pipeline to convert raw audio content into text. The model processes the input audio sampled at 16 kHz and outputs the corresponding text transcription, making it ideal for tasks like transcription, voice command recognition, and more.</p>
+</div>
+""", unsafe_allow_html=True)
+
+# Model Information Section
+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_whisper_small_english</td>
+        </tr>
+        <tr>
+            <td><strong>Compatibility</strong></td>
+            <td>Spark NLP 5.1.4+, PySpark 3.4+</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>Model Size</strong></td>
+            <td>1.1 GB</td>
+        </tr>
+    </table>
+</div>
+""", unsafe_allow_html=True)
+
+# References Section
+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/2023/10/17/asr_whisper_small_english_en.html" target="_blank">Whisper Model on Spark NLP</a></li>
+        <li><a class="link" href="https://huggingface.co/openai/whisper-small.en" target="_blank">Whisper Model on Hugging Face</a></li>
+        <li><a class="link" href="https://arxiv.org/abs/2212.04356" target="_blank">Whisper Paper</a></li>
+        <li><a class="link" href="https://github.com/openai/whisper" target="_blank">Whisper 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>: 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>: Live discussion with the community and team</li>
+        <li><a class="link" href="https://github.com/JohnSnowLabs/spark-nlp" target="_blank">GitHub</a>: Bug reports, feature requests, and contributions</li>
+        <li><a class="link" href="https://medium.com/spark-nlp" target="_blank">Medium</a>: Spark NLP articles</li>
+        <li><a class="link" href="https://www.youtube.com/channel/UCmFOjlpYEhxf_wJUDuz6xxQ/videos" target="_blank">YouTube</a>: Video tutorials</li>
+    </ul>
+</div>
+""", unsafe_allow_html=True)

requirements.txt

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+streamlit
+spark-nlp
+pyspark
+librosa
+pandas