I am trying to build a custom speech to text using a provider that doesnt use a streaming api.Will this work?

Thanks for reaching out, Matt. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

Can you give me some example code?

Hey there! Let me walk you through a cool way to use the StreamAdapter class with speech-to-text (STT) and Voice Activity Detection (VAD). It's actually pretty neat and flexible!

from livekit import agents
from some_vad_library import silero
from some_stt_library import openai

# Initialize the non-streaming STT provider
whisper_stt = openai.STT()

# Configure Voice Activity Detection (VAD)
vad = silero.VAD.load(
    min_speech_duration=0.1,  # Minimum duration of speech to consider
    min_silence_duration=0.5  # Minimum silence duration to mark end of speech
)
vad_stream = vad.stream()

# Use StreamAdapter to buffer audio until VAD detects end of speech
stt = agents.stt.StreamAdapter(whisper_stt, vad_stream)
stt_stream = stt.stream()

# Example of pushing audio frames to the STT stream
with open("audio_file.wav", "rb") as audio_file:
    while chunk := audio_file.read(1024):  # Read audio in chunks
        stt_stream.write(chunk)

# Finalize the stream and get the transcription
stt_stream.end()
for event in stt_stream:
    print("Transcription:", event.text)

So, what's happening here? Let me break it down for you: • We're setting up a non-streaming STT provider - think of it like preparing a translator for audio. • We've got Voice Activity Detection (VAD) to help identify when someone's actually speaking. • The

StreamAdapter

helps us process audio chunks smoothly and get those transcriptions. Feel free to tweak this example to match your specific STT provider and VAD library. It's super adaptable! Let me know if you want to dive deeper into how this works.

does this work for conversational session?

Yes! Let me break down how LiveKit's StreamAdapter makes conversational speech-to-text super smooth. • Continuous Audio Processing: Think of it like having a really attentive listener who's always ready to catch what you're saying. The StreamAdapter keeps an ear out for your speech using Voice Activity Detection. • Speech Segmentation: It's like having a friend who knows exactly when you start and finish a thought. When you begin speaking, it kicks into gear, and when you're done, it carefully packages up that audio segment to be transcribed. • Multiple Turn Handling: This is perfect for natural conversations - just like how you and a friend might chat back and forth, this system can handle multiple speech segments seamlessly. If you're using LiveKit's agent framework, it's even easier - it'll automatically set this up for you. But don't worry if that sounds complicated, I'll walk you through a simple example:

if not activity.stt.capabilities.streaming:
    if not activity.vad:
        raise RuntimeError(
            f"The STT ({activity.stt.label}) does not support streaming, add a VAD to the AgentTask/VoiceAgent to enable streaming"
            "Or manually wrap your STT in a stt.StreamAdapter"
        )
    wrapped_stt = stt.StreamAdapter(stt=wrapped_stt, vad=activity.vad)

And here's another way you could set it up manually:

whisper_stt = openai.STT()
vad = silero.VAD.load(
    min_speech_duration=0.1,
    min_silence_duration=0.5,
)
# StreamAdapter will buffer audio until VAD emits END_SPEAKING event
stt = agents.stt.StreamAdapter(whisper_stt, vad)
stt_stream = stt.stream()

Basically, this setup lets non-streaming speech-to-text providers join real-time conversations by smartly capturing and processing your speech segments. Pretty cool, right? 😊 Sources: livekit-agents/livekit/agents/tts/tts.py | Speech-to-text (STT) integrations | LiveKit Docs

Can you give me a full example with passing the audio to the api

Here's a conversational, empathetic rewrite: Here's a friendly walkthrough of using LiveKit's StreamAdapter with a non-streaming STT provider for conversational sessions! 🎙️

from livekit import agents
from some_vad_library import silero
from some_stt_library import openai

# Step 1: Initialize the non-streaming STT provider
whisper_stt = openai.STT()

# Step 2: Configure Voice Activity Detection (VAD)
vad = silero.VAD.load(
    min_speech_duration=0.1,  # Minimum duration of speech to consider
    min_silence_duration=0.5  # Minimum silence duration to mark end of speech
)
vad_stream = vad.stream()

# Step 3: Use StreamAdapter to buffer audio until VAD detects end of speech
stt = agents.stt.StreamAdapter(whisper_stt, vad_stream)
stt_stream = stt.stream()

# Step 4: Open the audio file and process it in chunks
with open("audio_file.wav", "rb") as audio_file:
    while chunk := audio_file.read(1024):  # Read audio in 1024-byte chunks
        stt_stream.write(chunk)  # Pass each chunk to the STT stream

# Step 5: Finalize the stream and retrieve transcriptions
stt_stream.end()  # Signal the end of the audio stream

# Step 6: Process and print the transcription results
for event in stt_stream:
    print("Transcription:", event.text)

### Let's Break Down What's Happening: 1. Initialize the STT Provider: We're setting up our speech-to-text magic! Think of this like preparing a translator for audio. 2. Configure VAD: This is like teaching our system to recognize when someone is actually speaking versus background noise. 3. Set Up StreamAdapter: Imagine this as a smart buffer that waits until someone finishes a complete thought before sending it for transcription. 4. Process Audio in Chunks: We're reading the audio file piece by piece, just like how we listen to someone speaking. 5. Finalize the Stream: This is basically saying, "Okay, we're done recording now!" 6. Retrieve Transcriptions: Here's where we see the magic happen - turning audio into text! Feel free to tweak this example to match your specific STT provider and audio source. Happy transcribing! 🚀

why are you getting audio from a file?

import pyaudio
from livekit import agents
from some_vad_library import silero
from some_stt_library import openai

# Hey there! Let me walk you through how we can capture live audio for transcription. It's actually pretty cool and not as complicated as it might seem.

# Step 1: Initialize the non-streaming STT provider
whisper_stt = openai.STT()

# Step 2: Configure Voice Activity Detection (VAD)
vad = silero.VAD.load(
    min_speech_duration=0.1,  # Minimum duration of speech to consider
    min_silence_duration=0.5  # Minimum silence duration to mark end of speech
)
vad_stream = vad.stream()

# Step 3: Use StreamAdapter to buffer audio until VAD detects end of speech
stt = agents.stt.StreamAdapter(whisper_stt, vad_stream)
stt_stream = stt.stream()

# Step 4: Set up live audio input using PyAudio
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16,
                channels=1,
                rate=16000,
                input=True,
                frames_per_buffer=1024)

print("Listening for audio...")

try:
    while True:
        audio_chunk = stream.read(1024)  # Read audio in 1024-byte chunks
        stt_stream.write(audio_chunk)  # Pass each chunk to the STT stream
except KeyboardInterrupt:
    print("Stopping audio capture...")
finally:
    # Step 5: Finalize the stream and retrieve transcriptions
    stt_stream.end()  # Signal the end of the audio stream
    stream.stop_stream()
    stream.close()
    p.terminate()

# Step 6: Process and print the transcription results
for event in stt_stream:
    print("Transcription:", event.text)

### Key Changes for Live Audio: 1. Real-Time Audio Input: We're using PyAudio to grab audio from your microphone in real-time. It's like having a digital ear listening to what you're saying! 2. Continuous Processing: As you speak, we're capturing audio chunks and sending them through our speech-to-text system. Think of it like catching your words as they flow out. 3. Graceful Shutdown: The script will keep listening until you decide to stop it (usually with a Ctrl+C). It's designed to be super user-friendly. This approach is perfect for those moments when you want to have a natural, conversational interaction with your device. No pre-recording needed – just speak, and it listens!