### Install FFmpeg for Whisper STT (Bash)

Source: https://github.com/progressing-llama/whisper-live-stt/blob/master/README.md

Installs the FFmpeg multimedia framework, a crucial dependency for the Whisper model to process audio effectively. Instructions are provided for various operating systems including Ubuntu/Debian, Arch Linux, macOS, and Windows.

```bash
# on Ubuntu or Debian
sudo apt update && sudo apt install ffmpeg

# on Arch Linux
sudo pacman -S ffmpeg

# on MacOS using Homebrew (https://brew.sh/)
brew install ffmpeg

# on Windows using Chocolatey (https://chocolatey.org/)
choco install ffmpeg

# on Windows using Scoop (https://scoop.sh/)
scoop install ffmpeg
```

--------------------------------

### Install Whisper Live STT Dependencies (Bash)

Source: https://github.com/progressing-llama/whisper-live-stt/blob/master/README.md

Installs the core Python libraries required for live speech-to-text transcription using Whisper. This includes the openai-whisper package itself, along with pyaudio for audio input, librosa for audio processing, and numpy for numerical operations.

```bash
pip install -U openai-whisper
pip install pyaudio
pip install librosa
pip install numpy
```

--------------------------------

### Start and Monitor Live Transcription

Source: https://context7.com/progressing-llama/whisper-live-stt/llms.txt

Starts the real-time transcription engine, which utilizes daemon threads for audio capture, processing, and transcript building. The transcribed text can be polled periodically.

```python
import os
import time
from live_stt_2 import LiveSTT

print("Initializing...")
stt = LiveSTT()

print("Running")

def clear_screen():
    if os.name == 'nt':
        _ = os.system('cls')
    else:
        _ = os.system('clear')

# Start the transcription engine
stt.start()

# Poll for transcribed text
while True:
    time.sleep(2)
    clear_screen()

    print("Predicted Text:")
    print(stt.confirmed_text)

    # Access current prediction
    print("\nCurrent Segment:")
    print(stt.current_predicted_text)
```

--------------------------------

### Run Live Speech-to-Text Transcription (Python)

Source: https://github.com/progressing-llama/whisper-live-stt/blob/master/README.md

Demonstrates how to initialize and run the live speech-to-text transcription service using the `LiveSTT` class. It includes functions to clear the console screen and continuously prints the confirmed transcribed text as it becomes available.

```python
import os
import time

from live_stt_2 import LiveSTT

print("Initializing...")

stt = LiveSTT()
#stt.calculate_recommended_settings(5)

print("Running")

def clear_screen():
    # For Windows
    if os.name == 'nt':
        _ = os.system('cls')
    # For macOS and Linux
    else:
        _ = os.system('clear')

stt.start()

while True:
    time.sleep(2)
    clear_screen()

    print("Predicted Text:")
    print(stt.confirmed_text)
```

--------------------------------

### Initialize LiveSTT Transcription Engine

Source: https://context7.com/progressing-llama/whisper-live-stt/llms.txt

Initializes the main class for real-time speech transcription. Allows configuration of processing and capture window durations, as well as the Whisper model. The model is automatically loaded to a CUDA GPU if available.

```python
from live_stt_2 import LiveSTT

# Initialize with default settings (4s processing, 8s capture window, turbo model)
stt = LiveSTT()

# Or customize parameters
stt = LiveSTT(
    processing_time=4,      # Time between processing steps in seconds
    capture_time=8,         # Audio capture window size in seconds
    whisper_model="turbo"   # Options: "tiny", "base", "small", "medium", "large", "turbo"
)

# The model is automatically loaded to CUDA GPU
# Window size and step size are calculated based on audio frequency (44.1kHz) and chunk size (2048)
```

--------------------------------

### Record Audio Chunks with PyAudio

Source: https://context7.com/progressing-llama/whisper-live-stt/llms.txt

Captures raw audio data in chunks using PyAudio. Configurable sample rate and buffer size allow control over audio input parameters. Ensure the recorder is always closed after use.

```python
from AudioInput import AudioRecorder

# Initialize audio recorder
recorder = AudioRecorder(
    frequency=44100,  # Sample rate in Hz
    chunk=1024        # Buffer size per read
)

# Capture audio chunks
try:
    while True:
        # Returns raw audio data as bytes (16-bit PCM)
        audio_chunk = recorder.record()
        # Process audio_chunk...
except KeyboardInterrupt:
    pass
finally:
    # Always close the recorder
    recorder.close()
```

--------------------------------

### Manually Transcribe Audio Window with Whisper (Python)

Source: https://context7.com/progressing-llama/whisper-live-stt/llms.txt

This snippet shows how to directly transcribe a specific audio window using the Whisper model with the LiveSTT class. It takes raw audio frames and a TimeFrame object as input, returning the transcribed text. The result is also added to an internal queue with metadata.

```python
from live_stt_2 import LiveSTT, TimeFrame
import numpy as np

stt = LiveSTT(whisper_model="turbo")
stt.start()

# process_data: list of raw audio byte frames
# Each frame is bytes object from AudioRecorder
process_data = [...]  # Your audio frames
time_frame = TimeFrame(start=0.0, end=8.0)

# Transcribe the audio window
# Returns transcribed text and adds result to queue
transcribed_text = stt.transcribe(process_data, time_frame)

print(f"Transcription: {transcribed_text}")

# The result is also added to internal queue with metadata:
# {
#     "text": transcribed_text,
#     "weight": confidence_score,
#     "time_frame": time_frame
# }
```

--------------------------------

### Manage TimeFrame Boundaries

Source: https://context7.com/progressing-llama/whisper-live-stt/llms.txt

A utility class for defining and managing temporal boundaries of audio segments. It provides methods to check for time intersections between segments and access segment duration.

```python
from live_stt_2 import TimeFrame

# Create time frames representing audio segments
frame1 = TimeFrame(start=0.0, end=5.0)   # 0-5 seconds
frame2 = TimeFrame(start=4.0, end=9.0)   # 4-9 seconds
frame3 = TimeFrame(start=10.0, end=15.0) # 10-15 seconds

# Check if frames intersect (overlap in time)
if frame1.intersects(frame2):
    print(f"Frames overlap")  # True - they overlap from 4-5 seconds

if frame1.intersects(frame3):
    print(f"Frames overlap")  # False - no overlap

# Access properties
print(f"Duration: {frame1.duration} seconds")  # 5.0
```

--------------------------------

### Detect Speech in Audio Frames with WebRTC VAD (Python)

Source: https://context7.com/progressing-llama/whisper-live-stt/llms.txt

This snippet demonstrates how to use the LiveSTT class to detect speech in raw audio frames using WebRTC VAD. It filters out silence to optimize transcription processing. The function `detect_speech_in_frames` returns a boolean indicating whether speech was detected.

```python
from live_stt_2 import LiveSTT
import numpy as np

stt = LiveSTT()

# audio_frames is a list of raw audio byte data at 44.1kHz
audio_frames = [...]  # Your captured audio frames

# Returns True if speech detected (>30% of frames contain speech)
has_speech = stt.detect_speech_in_frames(audio_frames)

if has_speech:
    # Process the audio for transcription
    print("Speech detected, transcribing...")
else:
    # Skip transcription to save compute
    print("Silence detected, skipping...")
```

--------------------------------

### Intelligently Concatenate Text Segments

Source: https://context7.com/progressing-llama/whisper-live-stt/llms.txt

Provides a function for merging two text strings intelligently, considering punctuation and capitalization rules to ensure natural sentence flow. Handles cases like leading punctuation, trailing periods, and sentence capitalization.

```python
from whisper_correction import intelligent_concatenate

# Rule 1: String2 starts with punctuation
text1 = "Hello world."
text2 = ", how are you"
result = intelligent_concatenate(text1, text2)
print(result)  # "Hello world, how are you"

# Rule 2: String2 starts lowercase, string1 ends with period
text1 = "Hello world."
text2 = "and welcome"
result = intelligent_concatenate(text1, text2)
print(result)  # "Hello world and welcome"

# Rule 3: String2 starts uppercase, string1 has no punctuation
text1 = "Hello world"
text2 = "Welcome home"
result = intelligent_concatenate(text1, text2)
print(result)  # "Hello world. Welcome home"

# Default: Simple space concatenation
text1 = "hello"
text2 = "world"
result = intelligent_concatenate(text1, text2)
print(result)  # "hello world"
```

--------------------------------

### Stop Live Transcription Engine

Source: https://context7.com/progressing-llama/whisper-live-stt/llms.txt

Gracefully stops the real-time transcription engine and cleans up associated resources. The `stop()` method can be called explicitly, or resource cleanup can be left to the `__del__` method.

```python
from live_stt_2 import LiveSTT

stt = LiveSTT()
stt.start()

# ... do transcription work ...

# Stop transcription
stt.stop()

# The __del__ method automatically handles cleanup
# but you can manually call stop() when needed
```

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