Raspberry Pi Zero 2W VOST STT: Sisyphus and the Repeating Buffer

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Raspberry Pi Zero 2W VOST STT: Sisyphus and the Repeating Buffer

This report analyzes the performance of VOST speech-to-text (STT) on a Raspberry Pi Zero 2W, focusing on the issue of repeating buffer ends in the generated transcripts. The user (@username) is aiming for clean STT output from common English dialog and has observed that VOST, processing a stream of overlapping buffers, exhibits this undesirable repetition. We will explore potential causes, suggest code modifications, and examine alternative STT solutions suitable for the Pi Zero 2W’s constrained resources.

Problem Deep Dive: Repeating Buffer Ends

The provided example transcripts clearly demonstrate the repeating buffer end issue:

  • “oh reallylly”
  • “karma we might need to kept those had a certain pointint”
  • “yeaheah”
  • “just might need to do thathat”
  • “am i right now it doesn’t appear to be sibiu boundundce”
  • “or it so i probably our pops watch face up to two hundredred”
  • “see i did rebooted quite a few times as i was working with audiodio”
  • “i wonder if there is is”
  • “some work doneone”
  • “i could runrun”
  • “big a i am as and see what i getget”
  • “hereere”

This indicates that the KaldiRecognizer is likely processing portions of the audio buffer multiple times, leading to the duplication of the final syllables or words. Several factors can contribute to this:

  1. Small Chunk Size: The CHUNK size (1024 bytes) might be too small for the speech rate and the model’s acoustic window. A small chunk means more frequent processing, and the recognizer might be “catching” the tail end of an utterance in one chunk and then again at the beginning of the next.

  2. Lack of Voice Activity Detection (VAD): The current code processes every chunk of audio, regardless of whether it contains speech. This means the recognizer is attempting to interpret silence and background noise, potentially triggering misinterpretations and exacerbating the repeating issue. Silence is a strong indicator of the end of an utterance.

  3. Overlapping Buffers: While @username notes the stream is intended to have overlapping buffers, the provided code doesn’t explicitly implement any overlap management. Overlap, if handled incorrectly, can easily lead to reprocessing the same audio segments. Without knowing the exact nature of the overlap, it’s difficult to pinpoint the issue, but it’s a prime suspect.

  4. Vosk Model Limitations: While VOST is generally accurate, the specific model being used might have limitations. Some models are more susceptible to errors with noisy audio or rapid speech. The model path ./model suggests it’s a local, possibly custom-trained, model. This can influence the outcome.

  5. Raspberry Pi Zero 2W Resource Constraints: The Pi Zero 2W, while an improvement over the original, still has limited processing power and memory. The real-time STT process is computationally intensive and may struggle to keep up, leading to timing issues and errors. The provided it could even use a little more memory of game using of thirty six mega swap out of a hundred suggests RAM is limited.

Code Analysis and Suggested Improvements

Let’s examine the Python code and suggest modifications to address these issues:

#!/usr/bin/env python3

import pyaudio
from vosk import Model, KaldiRecognizer
import json
import webrtcvad
import collections

# Path to the Vosk model directory
model_path = "./model"  # Replace with your model path

# Load the Vosk model
model = Model(model_path)

# Audio settings
FORMAT = pyaudio.paInt16  # 16-bit audio
CHANNELS = 1  # Mono
RATE = 16000  # Sample rate (match your model)
CHUNK = 1024  # Buffer size

# Initialize PyAudio
audio = pyaudio.PyAudio()

# Start microphone stream
stream = audio.open(format=FORMAT, channels=CHANNELS, rate=RATE,
                    input=True, frames_per_buffer=CHUNK)

# Initialize the recognizer
rec = KaldiRecognizer(model, RATE)

# Initialize VAD (Voice Activity Detection)
vad = webrtcvad.Vad(3)  # Aggressiveness: 0 (least aggressive) to 3 (most aggressive)
frame_duration = CHUNK / float(RATE) # duration of frame in seconds

#Ring Buffer
num_padding_frames = 10
ring_buffer = collections.deque(maxlen=num_padding_frames)

print("Listening... (Ctrl+C to stop)")

# Process the audio stream
try:
    while True:
        data = stream.read(CHUNK, exception_on_overflow=False)
        is_speech = vad.is_speech(data, RATE)

        ring_buffer.append((data, is_speech))

        triggered = False

        for frame in ring_buffer:
            if not triggered and frame[1]:
                triggered = True

        if triggered:
            for frame in ring_buffer:
                rec.AcceptWaveform(frame[0])
            result = json.loads(rec.Result())
            print("Text:", result.get("text", ""))
            ring_buffer.clear()
            rec.Reset()  # Clear the recognizer after processing

        else:
            partial_result = json.loads(rec.PartialResult())
            #print("Partial:", partial_result.get("partial", ""), end='\r')
except KeyboardInterrupt:
    # Clean up
    stream.stop_stream()
    stream.close()
    audio.terminate()
    final_result = json.loads(rec.FinalResult())
    print("\nFinal Text:", final_result.get("text", ""))

Key Changes and Explanations:

  1. Voice Activity Detection (VAD):

    • We integrate the webrtcvad library for VAD. pip install webrtcvad is required.
    • The vad = webrtcvad.Vad(3) initializes the VAD with an aggressiveness level. Adjust this value (0-3) based on the noise level in your environment. Higher values are more aggressive in filtering out non-speech.
    • vad.is_speech(data, RATE) determines whether the current chunk contains speech. We use this to avoid processing silence.
    • Added ring buffer to delay processing until speech is detected. This might help capture beginnings of speech that get missed.

  2. rec.Reset(): Crucially, the rec.Reset() method is called after processing each full utterance. This clears the internal state of the recognizer, preventing it from carrying over partial results from one utterance to the next, which is a major cause of repetition.

  3. Conditional Processing: The code now only calls rec.AcceptWaveform() and prints the result if the VAD indicates speech is present. This reduces the load on the recognizer and minimizes the chances of misinterpreting silence.

  4. Chunk Size: While I haven’t increased the Chunk Size here for resource reasons, testing with larger chunks may improve results.

  5. Ring Buffer: Implementation of a ring buffer will help capture the beginning of speech that otherwise might be missed.

Further Optimizations and Considerations:

  • Overlapping Buffer Management: To properly handle overlapping buffers, you need a more sophisticated approach. Consider implementing a sliding window technique. Instead of simply reading a fixed-size CHUNK from the stream, maintain a larger buffer that overlaps with previous reads. Then, use VAD or a similar technique to determine the optimal start and end points for processing.

  • Adaptive Chunk Size: Experiment with dynamically adjusting the CHUNK size based on speech activity. For example, during periods of silence, you could temporarily increase the CHUNK size to reduce the frequency of processing. When speech is detected, revert to a smaller CHUNK size for better responsiveness.

  • Speech Rate Analysis: Analyze the speech rate and adjust the CHUNK size and VAD aggressiveness accordingly. Faster speech might benefit from smaller chunks, while slower speech could tolerate larger chunks.

  • Resource Monitoring: Use tools like htop to monitor the Pi Zero 2W’s CPU and memory usage. If the system is consistently near its limits, you might need to further optimize the code or consider a more powerful device.

Alternative STT Solutions for Raspberry Pi Zero 2W

Given the resource constraints of the Pi Zero 2W, it might be worth exploring alternative STT solutions:

  1. Picovoice Leopard: Picovoice offers Leopard, an on-device STT engine that is specifically designed for low-resource devices like the Raspberry Pi. It boasts high accuracy and low latency. This can be worth the cost of paying for an STT SDK solution if the results are better than free, open source solutions.

    • Pros: Specifically designed for low-resource devices, high accuracy.
    • Cons: Commercial product (requires a license).

  2. DeepSpeech (Mozilla): While Mozilla discontinued DeepSpeech, the open-source code and trained models are still available. However, running DeepSpeech on a Pi Zero 2W might be challenging due to its resource requirements. It would require significant optimization.

    • Pros: Open-source, potentially high accuracy (depending on the model).
    • Cons: Resource-intensive, may require significant optimization for the Pi Zero 2W.

  3. Cloud-Based STT (with local pre-processing): Consider offloading the STT processing to a cloud service like Google Cloud Speech-to-Text or AWS Transcribe. The Pi Zero 2W would be responsible for capturing the audio, performing basic pre-processing (e.g., noise reduction), and then sending the audio data to the cloud for transcription. This would require a reliable internet connection.

    • Pros: High accuracy (using cloud resources), minimal load on the Pi Zero 2W.
    • Cons: Requires internet connectivity, potential latency, privacy concerns.

    Example (Illustrative - using Google Cloud Speech-to-Text):

    # This is a VERY simplified example - you'll need to install the Google Cloud Speech library
# and configure authentication

import pyaudio
from google.cloud import speech_v1 as speech

# (Authentication setup would go here - see Google Cloud documentation)
client = speech.SpeechClient()

FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 16000
CHUNK = 1024

audio = pyaudio.PyAudio()
stream = audio.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK)

config = speech.RecognitionConfig(
    encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
    sample_rate_hertz=RATE,
    language_code="en-US",
)

streaming_config = speech.StreamingRecognitionConfig(config=config, interim_results=True)

def generator():
    while True:
        data = stream.read(CHUNK)
        yield speech.StreamingRecognizeRequest(audio_content=data)

requests = (speech.StreamingRecognizeRequest(audio_content=chunk) for chunk in generator())

responses = client.streaming_recognize(config=streaming_config, requests=requests)

try:
    for response in responses:
        for result in response.results:
            if result.is_final:
                print("Transcript: {}".format(result.alternatives[0].transcript))
except KeyboardInterrupt:
    stream.stop_stream()
    stream.close()
    audio.terminate()

Important Considerations for Cloud-Based Solutions:

  • Latency: Cloud-based STT introduces latency due to network communication. This can be a concern for real-time applications.
  • Cost: Cloud STT services typically charge based on usage. Consider the cost implications before deploying a cloud-based solution.
  • Privacy: Sending audio data to the cloud raises privacy concerns. Evaluate whether a cloud-based solution is acceptable from a privacy perspective.
  1. Whisper: OpenAI’s Whisper models are impressive, but may also be too large to run directly on a RPi Zero 2W.

Conclusion

Improving VOST’s STT performance on a Raspberry Pi Zero 2W requires a multi-pronged approach. Addressing the repeating buffer end issue involves careful tuning of the audio processing parameters, integrating Voice Activity Detection, and potentially switching to a different VOST model or an alternative STT engine entirely. The code modifications suggested in this report are a starting point. Further experimentation and optimization are needed to achieve the desired level of accuracy and responsiveness. Given the limited resources of the Pi Zero 2W, exploring cloud-based solutions or commercial embedded solutions like Picovoice Leopard may also be viable options. Ultimately, the best approach will depend on the specific requirements of the application and the acceptable trade-offs between accuracy, latency, cost, and privacy. @username should experiment with multiple approaches before deciding which one is the best fit.

#vost stt #raspberrypi
#speechtotext #opensource #embedded
AI #machinelearning #python

yakyak:{“make”: “gemini”, “model”: “gemini-2.0-flash”}