recognize_openai()

recognize_openai(
    audio_data: AudioData,
    model: Literal["whisper-1", "gpt-4o-transcribe", "gpt-4o-mini-transcribe"] = "whisper-1",
    language: str | None = None,
    prompt: str | None = None,
    response_format: Literal["json"] = "json",
    temperature: float | None = None
) -> str

import speech_recognition as sr
import os

# Set API key
os.environ["OPENAI_API_KEY"] = "sk-..."

# Initialize recognizer
r = sr.Recognizer()

# Record audio
with sr.Microphone() as source:
    print("Say something!")
    audio = r.listen(source)

# Recognize with OpenAI Whisper API
try:
    text = r.recognize_openai(audio)
    print(f"You said: {text}")
except Exception as e:
    print(f"Error: {e}")

import speech_recognition as sr
import os

os.environ["OPENAI_API_KEY"] = "sk-..."

r = sr.Recognizer()

with sr.Microphone() as source:
    print("Diga algo...")
    audio = r.listen(source)

# Specify Spanish language for better accuracy
text = r.recognize_openai(audio, language="es")
print(f"Usted dijo: {text}")

import speech_recognition as sr
import os

os.environ["OPENAI_API_KEY"] = "sk-..."

r = sr.Recognizer()

with sr.Microphone() as source:
    audio = r.listen(source)

# Use GPT-4o for higher accuracy
text = r.recognize_openai(audio, model="gpt-4o-transcribe")
print(f"Transcript: {text}")

import speech_recognition as sr
import os

os.environ["OPENAI_API_KEY"] = "sk-..."

r = sr.Recognizer()

with sr.Microphone() as source:
    audio = r.listen(source)

# Provide context or spelling guidance
prompt = "This is a technical discussion about Kubernetes, TensorFlow, and microservices."

text = r.recognize_openai(audio, prompt=prompt)
print(f"Transcript: {text}")

import speech_recognition as sr
import os

os.environ["OPENAI_API_KEY"] = "sk-..."

r = sr.Recognizer()

# Load audio file
with sr.AudioFile("meeting.wav") as source:
    audio = r.record(source)

# Transcribe
text = r.recognize_openai(audio, model="whisper-1")
print(text)

import speech_recognition as sr
import os

# Configure for self-hosted OpenAI-compatible endpoint
os.environ["OPENAI_API_KEY"] = "dummy-key"  # Use any value
os.environ["OPENAI_BASE_URL"] = "http://localhost:8000/v1"

r = sr.Recognizer()

with sr.Microphone() as source:
    audio = r.listen(source)

text = r.recognize_openai(audio)
print(text)

import speech_recognition as sr
import os
from pathlib import Path

os.environ["OPENAI_API_KEY"] = "sk-..."

r = sr.Recognizer()

# Process all audio files in a directory
audio_files = Path("recordings").glob("*.wav")

for audio_file in audio_files:
    with sr.AudioFile(str(audio_file)) as source:
        audio = r.record(source)
    
    try:
        text = r.recognize_openai(audio, language="en")
        print(f"{audio_file.name}: {text}")
    except Exception as e:
        print(f"Error processing {audio_file.name}: {e}")

import speech_recognition as sr
import os
from openai import OpenAIError, AuthenticationError, RateLimitError

os.environ["OPENAI_API_KEY"] = "sk-..."

r = sr.Recognizer()

with sr.Microphone() as source:
    audio = r.listen(source)

try:
    text = r.recognize_openai(audio)
    print(f"Transcript: {text}")
except AuthenticationError:
    print("Invalid API key")
except RateLimitError:
    print("Rate limit exceeded. Please wait and retry.")
except OpenAIError as e:
    print(f"OpenAI API error: {e}")
except sr.SetupError:
    print("OpenAI library not installed. Run: pip install openai")

pip install openai

export OPENAI_API_KEY="sk-..."

import os
os.environ["OPENAI_API_KEY"] = "sk-..."

# .env file
OPENAI_API_KEY=sk-...

from dotenv import load_dotenv
load_dotenv()

# Start vLLM server with Whisper model
vllm serve openai/whisper-large-v3 --port 8000

import os
import speech_recognition as sr

os.environ["OPENAI_API_KEY"] = "dummy"  # Any value works
os.environ["OPENAI_BASE_URL"] = "http://localhost:8000/v1"

r = sr.Recognizer()

with sr.Microphone() as source:
    audio = r.listen(source)

text = r.recognize_openai(audio)
print(text)