Whisper-based speech recognition functionality as a Flask endpoint (Research)

Check out all on 'Automatic Speech Recognition'

Part 1

Dated: March 2025

I want to run a Speech Recognition app as a flask endpoint?

Below is an example of how to expose your Whisper-based speech recognition functionality as a Flask endpoint. In this example, the server accepts an audio file via a POST request, processes it with the Whisper model, and returns the transcription as JSON.

---

Example: app.py

    
from flask import Flask, request, jsonify
import whisper
import os
import uuid
import soundfile as sf

app = Flask(__name__)

# Load the Whisper model (choose "base", "small", "medium", or "large")
model = whisper.load_model("base")

@app.route("/transcribe", methods=["POST"])
def transcribe():
    # Check if an audio file is included in the request
    if "audio" not in request.files:
        return jsonify({"error": "No audio file provided."}), 400

    audio_file = request.files["audio"]
    # Generate a unique filename to temporarily store the audio file
    temp_filename = f"temp_{uuid.uuid4().hex}.wav"
    audio_file.save(temp_filename)

    try:
        # Optional: Ensure the audio is 16kHz. 
        # This reads the file and, if needed, rewrites it with the correct sample rate.
        audio_data, samplerate = sf.read(temp_filename)
        if samplerate != 16000:
            sf.write(temp_filename, audio_data, 16000)
    except Exception as e:
        os.remove(temp_filename)
        return jsonify({"error": "Failed to process audio file", "details": str(e)}), 500

    try:
        # Run transcription using Whisper
        result = model.transcribe(temp_filename)
        transcription = result.get("text", "")
    except Exception as e:
        os.remove(temp_filename)
        return jsonify({"error": "Transcription failed", "details": str(e)}), 500

    # Clean up the temporary file
    os.remove(temp_filename)
    return jsonify({"transcription": transcription})

if __name__ == "__main__":
    # Run the Flask server on 0.0.0.0 so it's accessible on your network
    app.run(debug=True, host="0.0.0.0", port=5000)

Apple unveils new Mac Studio that can support LLMs with over 600 billion parameters

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Apple emphasises AI capabilities of the Mac Studio, with the M3 Ultra model supporting large language models (LLMs) with over 600 billion parameters entirely in memory



Apple on Wednesday (March 5, 2025) announced the latest iteration of its Mac Studio, touting it as the most powerful Mac ever made. Equipped with the new M4 Max and M3 Ultra chips, the compact desktop is designed for professionals who require top-tier performance. The new Mac Studio introduces Thunderbolt 5 for faster connectivity, up to 512GB of unified memory, and 16TB of SSD storage—all within a compact and quiet design meant to sit right on a desk.

Apple claims the new Mac Studio provides massive performance gains compared to previous models, making it an ideal choice for users working with AI, video production, and other intensive tasks.

Apple emphasises AI capabilities of the Mac Studio, with the M3 Ultra model supporting large language models (LLMs) with over 600 billion parameters entirely in memory. This makes it a powerhouse for AI workloads, offering high efficiency for machine learning and creative applications. It is also optimised for Apple Intelligence, which enhances productivity and privacy. The new Mac Studio is available for pre-order today, with general availability starting March 12.

Mac Studio with M4 Max

The M4 Max version of Mac Studio is designed for video editors, developers, engineers, and creative professionals. It features a 16-core CPU, a 40-core GPU, and over 500GB/s of memory bandwidth, making it significantly faster than the M1 Max-based Mac Studio. Apple reports that the M4 Max delivers up to 3.5x the performance of the M1 Max model and is 6.1x faster than the most powerful Intel-based 27-inch iMac. With up to 128GB of unified memory, users can handle complex workflows, including large-scale image processing and high-resolution video editing.

Mac Studio with M3 Ultra

For those needing even greater power, the M3 Ultra version of the Mac Studio is the ultimate professional desktop. It boasts an up to 32-core CPU, an 80-core GPU, and 800GB/s of memory bandwidth, delivering nearly twice the performance of M4 Max in multi-threaded workloads. Apple claims that Mac Studio with M3 Ultra is 2.6x faster than its M1 Ultra predecessor and 6.4x faster than the Intel Xeon-based Mac Pro. It supports up to 512GB of unified memory, the highest ever in a personal computer, making it a game-changer for AI, video production, and 3D rendering.

Apple has upgraded the Mac Studio’s connectivity with Thunderbolt 5, offering speeds up to 120 Gb/s, three times faster than the previous generation. This allows for faster external storage, expansion chassis, and multi-display setups. The M3 Ultra model can drive up to eight Pro Display XDRs at full 6K resolution, making it ideal for professionals who require extensive screen real estate. The Mac Studio also includes a 10Gb Ethernet port, HDMI, an SDXC card slot, built-in Wi-Fi, and Bluetooth, providing a well-rounded set of connectivity options.

macOS Sequoia and Apple Intelligence

The new Mac Studio runs on macOS Sequoia, offering features like iPhone Mirroring, enhanced window management, and a redesigned Safari experience. With Apple Intelligence, users can take advantage of AI-powered writing tools, priority notifications, live transcription, and ChatGPT integration in Siri. Apple ensures that privacy remains a top priority, using on-device processing and Private Cloud Compute to handle AI tasks securely.

Mac Studio (2025) Key Specifications

Feature	Mac Studio with M4 Max	Mac Studio with M3 Ultra
CPU	16-core	32-core (24 performance cores)
GPU	40-core	80-core
Neural Engine	3x faster than M1 Max	32-core for AI/ML tasks
Memory	Up to 128GB unified memory	Up to 512GB unified memory
Feature	Mac Studio with M4 Max	Mac Studio with M3 Ultra
Storage	Up to 16TB SSD	Up to 16TB SSD
Memory Bandwidth	500GB/s+	800GB/s+
Connectivity	Thunderbolt 5, 10Gb Ethernet, HDMI, SDXC	Thunderbolt 5, 10Gb Ethernet, HDMI, SDXC
AI Performance	Runs LLMs efficiently	Handles 600B+ parameter models in memory
Display Support	Multiple 6K displays	Up to 8 Pro Display XDRs at 6K resolution

Ref

Tags: Technology,Generative AI,Large Language Models,Artificial Intelligence,

Day 1 With MySQL - Setting the DB up on PythonAnywhere cloud and running some tests

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How do I create a MySQL database in PythonAnywhere?

How to Create a MySQL Database in PythonAnywhere

PythonAnywhere provides a MySQL database for its users, but you need to set it up first. Follow these steps:

---

🔹 Step 1: Log in to PythonAnywhere

1. Go to PythonAnywhere and log in.
2. Navigate to the Databases tab.

---

🔹 Step 2: Create a New MySQL Database

1. Click "Add a new MySQL database".
2. Choose a database name (e.g., mydatabase).
3. Click Create.
4. PythonAnywhere will generate:
   • A database name (e.g., your_username$mydatabase).
   • A MySQL username (same as your PythonAnywhere username).
   • A MySQL password (auto-generated, but you can reset it).
   • A MySQL host (usually your_username.mysql.pythonanywhere-services.com).

---

🔹 Step 3: Connect to MySQL from PythonAnywhere Console

To check if the database is working, open a Bash console in PythonAnywhere and run:

bash
mysql -u your_username -h your_username.mysql.pythonanywhere-services.com -p

Enter your MySQL password when prompted.

To list databases:

sql
SHOW DATABASES;

To switch to your database:

sql
USE your_username$mydatabase;

---

🔹 Step 4: Connect to MySQL from a Flask App

Install mysql-connector-python (if not already installed):

bash
pip install mysql-connector-python

Then, create a Python script (app.py):

python
import mysql.connector

# Database credentials from PythonAnywhere
DB_CONFIG = {
    "host": "your_username.mysql.pythonanywhere-services.com",
    "user": "your_username",
    "password": "your_mysql_password",
    "database": "your_username$mydatabase"
}

# Connect to MySQL
try:
    db = mysql.connector.connect(**DB_CONFIG)
    cursor = db.cursor()
    print("Connected to MySQL successfully!")

    # Create a table (if needed)
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS users (
            id INT AUTO_INCREMENT PRIMARY KEY,
            name VARCHAR(100),
            email VARCHAR(100) UNIQUE
        )
    """)
    print("Table created!")

    # Close connection
    cursor.close()
    db.close()

except mysql.connector.Error as err:
    print(f"Error: {err}")

Run the script:

bash
python app.py

---

🔹 Step 5: Access MySQL from PythonAnywhere Web App

If you're deploying a Flask app on PythonAnywhere:

1. Go to "Web" and create a new Flask app.
2. Make sure app.py uses the correct MySQL hostname (not localhost but your_username.mysql.pythonanywhere-services.com).
3. Restart the web app after changes.

---

✅ Final Checklist

✔ Use your full database name (your_username$mydatabase).
✔ Use PythonAnywhere’s MySQL hostname (your_username.mysql.pythonanywhere-services.com).
✔ Avoid using localhost in your connection string.

Let me know if you need further help! 🚀

Testing the MySQL Database

mysql> use ashishjaindrive1$default
Database changed
mysql> 
mysql> create table users (name varchar(100));
Query OK, 0 rows affected (0.02 sec)

mysql> insert into users values('ram');
Query OK, 1 row affected (0.01 sec)

mysql> insert into users values('ravan');
Query OK, 1 row affected (0.00 sec)

mysql> commit;
Query OK, 0 rows affected (0.00 sec)

mysql> 



How to install python-dotenv?  

12:23 ~/mysite $ pip install python-dotenv
Defaulting to user installation because normal site-packages is not writeable
Looking in links: /usr/share/pip-wheels
Collecting python-dotenv
    Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)
Installing collected packages: python-dotenv
Successfully installed python-dotenv-1.0.1
12:25 ~/mysite $