ESP-SR: Troubleshooting Program Crashes With Multiple Models

by Alex Johnson 61 views

Experiencing crashes when working with multiple models in ESP-SR can be a frustrating issue. This article aims to provide a comprehensive guide to understanding and resolving such problems. We'll delve into a specific case reported by a user, analyze the potential causes, and offer systematic approaches to debugging and fixing these crashes.

The Issue: Crashes with Multiple Models

A user reported a consistent crashing issue when using multiple models in their ESP-SR project. Specifically, the program crashes when orchestrating two processes: one using the wn9_miaomiaotongxue_tts WakeNet model and another using the nsnet1 Noise Suppression Model. The user noted that the crashes occur consistently when multiple models are used, while the program runs without issues when only a single model is active. This suggests that the problem lies within the orchestration or interaction of these models, rather than the models themselves.

The user has already checked for similar issues, reviewed the documentation, and tested with the latest version of ESP-SR, confirming that the problem persists. This meticulous approach to troubleshooting is commendable and helps narrow down the potential causes.

Specific Observations

  • Crash Frequency: The bug occurs consistently whenever multiple models are used.
  • Crash Location: The crash location varies, making it harder to pinpoint the exact cause.
  • Model Combination: The issue arises when using the wn9_miaomiaotongxue_tts WakeNet model and the nsnet1 Noise Suppression Model together.
  • Single Model Stability: The program runs fine with only one model active, indicating a problem with multi-model orchestration.
  • Partition Size: The model partition size (6MB) is significantly larger than the combined size of the models (1.1MB), ruling out insufficient memory as the primary cause.
  • Reproduction Steps: Using the model/movemodel.py script to package the srmodels.bin file reliably triggers the crash.

Code Snippets and Logs

The user provided valuable log snippets that highlight the program's behavior leading up to the crash. Here's a breakdown of the key parts:

[15:31:31.283] I (12783) AFE_CONFIG: Set Noise Suppression Model: nsnet1
[15:31:31.289] I (12793) AFE_CONFIG: Set WakeNet Model: wn9_miaomiaotongxue_tts
[15:31:31.363] MC Quantized wakenet9: wakenet9l_tts1h8_喵喵同学_3_0.644_0.648, tigger:v4, mode:0, p:0, (Nov 11 2025 10:37:06)
[15:31:31.369] I (12873) AFE: AFE Version: (1MIC_V250121)
[15:31:31.374] I (12883) AFE: Input PCM Config: total 2 channels(1 microphone, 1 playback), sample rate:16000
[15:31:31.385] I (12883) AFE: AFE Pipeline: [input] -> |AEC(SR_HIGH_PERF)| -> ,)| -> |VAD(WebRTC)| -> |WakeNet(wn9_miaomiaotongxu
[15:31:31.391] e_tts,)| -> [output]

This log segment shows the successful loading and configuration of both the Noise Suppression Model (nsnet1) and the WakeNet Model (wn9_miaomiaotongxue_tts). The Audio Front-End (AFE) pipeline is also initialized, including components like Acoustic Echo Cancellation (AEC), Voice Activity Detection (VAD), and WakeNet. This suggests that the initial setup is likely not the source of the problem.

Another log snippet provides more detailed information about the nsnet1 model loading process:

[15:31:14.833] I (26643) AFE_CONFIG: Set Noise Suppression Model: nsnet1
[15:31:14.833] I (26643) AFE_CONFIG: Set WakeNet Model: wn9_miaomiaotongxue_tts
[15:31:14.955] I (26763) NSNET: model_name: nsnet1, model_data: nsnet1_data, info: nsnet1_v1_ns_0_0.0_0.0, (Nov 11 2025 10:37:08)
[15:31:14.961]
[15:31:14.982] I (26783) NSNET: step_num: 3, load_mode: 0, features_dim: 256
[15:31:14.982] I (26793) NSNET: in_conv size:1, rate:1, out_fbit:0, outa_fbit:0, feature dim:256, w:256, h:256
...
[15:31:15.208] I (27013) AFE: AFE Version: (1MIC_V250121)
[15:31:15.208] I (27013) AFE: Input PCM Config: total 2 channels(1 microphone, 1 playback), sample rate:16000
[15:31:15.219] I (27013) AFE: AFE Pipeline: [input] -> |AEC(VOIP_HIGH_PERF)| -> |NS(nsnet1)| -> |VAD(WebRTC)| -> [output]

This detailed log shows the successful loading and initialization of the nsnet1 model, including its various parameters and configurations. The AFE pipeline now includes the Noise Suppression (NS) component (nsnet1). Again, this suggests that the individual models are loading correctly.

Potential Causes and Troubleshooting Steps

Given the information, here are some potential causes for the crashes and steps to investigate them:

1. Memory Management Issues

Even though the model partition size seems sufficient, memory fragmentation or inefficient memory allocation could still be a factor. When multiple models are loaded and interact, the memory demands increase, potentially exposing memory-related bugs.

  • Troubleshooting Steps:
    • Heap Usage Analysis: Use ESP-IDF's built-in heap monitoring tools to track memory usage during model loading and orchestration. Look for signs of memory leaks or excessive memory consumption.
    • Memory Fragmentation: Investigate memory fragmentation by analyzing the heap state. Consider using techniques like memory pools or custom allocators to mitigate fragmentation.
    • Model Loading Order: Experiment with the order in which the models are loaded. Loading larger models first might help prevent fragmentation.

2. Concurrency and Synchronization

When multiple models are active, they might access shared resources concurrently, leading to race conditions or data corruption. This is especially likely if the models are running in separate tasks or interrupts.

  • Troubleshooting Steps:
    • FreeRTOS Analysis: Examine the FreeRTOS configuration and task priorities. Ensure that the tasks running the models are properly synchronized using mutexes, semaphores, or queues.
    • Interrupt Handling: If any model processing occurs within interrupt handlers, ensure that interrupt safety guidelines are followed and that shared resources are protected.
    • Thread-Safety: Verify that the model implementations are thread-safe. Look for potential race conditions when accessing global variables or shared data structures.

3. AFE Pipeline Configuration

The Audio Front-End (AFE) pipeline plays a crucial role in processing audio data for the models. Incorrect configuration or conflicts within the pipeline could lead to crashes.

  • Troubleshooting Steps:
    • Pipeline Inspection: Carefully review the AFE pipeline configuration, particularly the interaction between AEC, NS, VAD, and WakeNet components. Ensure that the data flow is correct and that the components are compatible.
    • Configuration Parameters: Check the parameters used for each AFE component, such as sampling rates, buffer sizes, and algorithm settings. Incorrect parameters can lead to unexpected behavior.
    • Component Conflicts: Look for potential conflicts between different AFE components. For example, some noise suppression algorithms might interfere with wake word detection.

4. Model Compatibility and Dependencies

Incompatibilities between the models themselves or their dependencies could also cause crashes. This is more likely if the models were developed independently or use different versions of underlying libraries.

  • Troubleshooting Steps:
    • Dependency Analysis: Identify the dependencies of each model and ensure that they are compatible with each other and with the ESP-IDF version.
    • Model Input/Output: Verify that the models are compatible in terms of input and output data formats. For example, the output of the noise suppression model should be suitable as input for the wake word detection model.
    • Version Mismatches: Check for version mismatches between the models, ESP-IDF, and any external libraries. Update or downgrade components as needed to ensure compatibility.

5. Bug in ESP-SR or Underlying Libraries

While less likely, there's always a possibility of a bug in ESP-SR or one of its underlying libraries. If all other troubleshooting steps fail, this possibility should be considered.

  • Troubleshooting Steps:
    • ESP-IDF Version: Try different versions of ESP-IDF to see if the issue is specific to a particular version.
    • Minimal Reproducible Example: Create a minimal, self-contained example that reproduces the crash. This will help isolate the problem and make it easier to report to the ESP-SR developers.
    • Community Support: Seek help from the ESP-SR community forums or GitHub issues. Other users might have encountered similar problems and found solutions.

Addressing the User's Specific Questions

The user raised two specific questions that deserve attention:

1. System Methods for Memory Crash Localization