Different outputs between file-based and mic-based streaming inference

[hassan-webm]

Hi, I’m trying to simulate file streaming for a NeMo streaming ASR model. Specifically this one nvidia/stt_en_fastconformer_hybrid_large_streaming_multi

I noticed that:

1. While using examples/asr/asr_cache_aware_streaming/speech_to_text_cache_aware_streaming_infer.py, which uses CacheAwareStreamingAudioBuffer and preprocesses the entire audio file, gives correct and consistent output.

2. Using the tutorials/asr/Online_ASR_Microphone_Demo_Cache_Aware_Streaming.ipynb, if I replace the mic input block with this file reader:

import wave
import numpy as np

AUDIO_FILE_PATH = "path/to/audio/file.wav"  # replace with your file path
SAMPLE_RATE = 16000

chunk_duration_ms = lookahead_size + ENCODER_STEP_LENGTH
chunk_samples = int(SAMPLE_RATE * chunk_duration_ms / 1000) 

print(f"Chunk size: {chunk_samples} samples ({chunk_duration_ms} ms)")

def load_audio_file(filepath, chunk_size):
    with wave.open(filepath, "rb") as wf:
        assert wf.getframerate() == SAMPLE_RATE, f"Expected {SAMPLE_RATE}, got {wf.getframerate()}"
        assert wf.getnchannels() == 1, "Only mono audio supported"
        while True:
            data = wf.readframes(chunk_size)
            if not data:
                break
            yield np.frombuffer(data, dtype=np.int16)

print("Simulating streaming from audio file...")

for chunk in load_audio_file(AUDIO_FILE_PATH, chunk_samples):
    text = transcribe_chunk(chunk)
    print(text, end="\r", flush=True)

print("\nStreaming simulation finished.")

then I get different (and less accurate) transcripts on each run, even though it’s the same file.

Why do these two approaches give such different results? Is the CacheAwareStreamingAudioBuffer doing something extra for chunk alignment or state management that’s missing from the mic demo?

Additional context My end goal is to build a WebSocket ASR service where I don’t have the entire audio file beforehand — instead, incremental audio chunks are received from clients in real-time. In that case, I cannot preprocess the whole file like in the CacheAwareStreamingAudioBuffer example.

What’s the recommended way to structure the streaming pipeline in this scenario, so the results remain consistent with the file-based example?

Thanks!

[decimal-agent]

This is an AI-generated response. Please verify.

The inconsistency between your file-based streaming simulation and the CacheAwareStreamingAudioBuffer approach is due to missing state management and proper chunk alignment.

The CacheAwareStreamingAudioBuffer class handles several critical functions that your custom implementation lacks:

1. Proper chunk alignment according to the model's requirements
2. State management between chunks (cached audio frames and encoder state)
3. Consistent preprocessing across chunks
4. Cache-aware processing that maintains context between chunks

For your WebSocket service, you should:

1. Initialize a CacheAwareStreamingAudioBuffer for each client connection
2. Initialize and maintain cache state variables between chunks
3. Process each incoming audio chunk through the buffer
4. Pass the proper cache state to each model inference step
5. Update the cache state after each step

This approach will ensure consistent results by maintaining proper context between audio chunks, just like the file-based example does. The key is treating each client's audio stream as a continuous process rather than independent chunks.