Turn spoken words into text.

What is Speech Recognition?

Convert audio into written text.

**Use cases**: Voice assistants, transcription, accessibility!

Using Speech Recognition

```python # Install # pip install SpeechRecognition

import speech_recognition as sr

Create recognizer recognizer = sr.Recognizer()

From microphone with sr.Microphone() as source: print("Speak something...") audio = recognizer.listen(source) try: text = recognizer.recognize_google(audio) print(f"You said: {text}") except sr.UnknownValueError: print("Could not understand audio") except sr.RequestError as e: print(f"Error: {e}") ```

From Audio File

```python # Load audio file with sr.AudioFile('audio.wav') as source: audio = recognizer.record(source) text = recognizer.recognize_google(audio) print(text) ```

Whisper (OpenAI)

State-of-the-art speech recognition:

```python # Install # pip install openai-whisper

import whisper

Load model model = whisper.load_model("base") # tiny, base, small, medium, large

Transcribe result = model.transcribe("audio.mp3") print(result["text"])

With timestamps segments = result["segments"] for segment in segments: start = segment["start"] end = segment["end"] text = segment["text"] print(f"[{start:.2f}s - {end:.2f}s]: {text}") ```

Real-time Transcription

```python import pyaudio import wave

Record audio def record_audio(filename, duration=5): CHUNK = 1024 FORMAT = pyaudio.paInt16 CHANNELS = 1 RATE = 16000 p = pyaudio.PyAudio() stream = p.open( format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK ) print("Recording...") frames = [] for _ in range(0, int(RATE / CHUNK * duration)): data = stream.read(CHUNK) frames.append(data) print("Done recording") stream.stop_stream() stream.close() p.terminate() # Save wf = wave.open(filename, 'wb') wf.setnchannels(CHANNELS) wf.setsampwidth(p.get_sample_size(FORMAT)) wf.setframerate(RATE) wf.writeframes(b''.join(frames)) wf.close()

Use it record_audio("recording.wav", duration=5) result = model.transcribe("recording.wav") print(result["text"]) ```

Different Languages

```python # Specify language result = model.transcribe("audio.mp3", language="es") # Spanish print(result["text"])

Auto-detect language result = model.transcribe("audio.mp3") print(f"Detected language: {result['language']}") ```

Voice Commands

```python def listen_for_command(): with sr.Microphone() as source: print("Listening...") audio = recognizer.listen(source) try: command = recognizer.recognize_google(audio).lower() return command except: return None

Command handler while True: command = listen_for_command() if command: if "hello" in command: print("Hello there!") elif "time" in command: from datetime import datetime now = datetime.now() print(f"Current time: {now.strftime('%H:%M')}") elif "stop" in command: print("Goodbye!") break else: print(f"You said: {command}") ```

Speaker Diarization

Identify who is speaking:

```python # Install # pip install pyannote.audio

from pyannote.audio import Pipeline

pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization")

Diarize diarization = pipeline("audio.wav")

Print results for turn, _, speaker in diarization.itertracks(yield_label=True): print(f"Speaker {speaker}: {turn.start:.1f}s - {turn.end:.1f}s") ```

Best Practices

**Good audio**: Minimize background noise **Sample rate**: Use 16kHz for most models **Segment long audio**: Break into smaller chunks **Post-processing**: Clean up text output

Remember

- Whisper is most accurate - Works with multiple languages - Needs clear audio for best results - Can run locally (no API needed)