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library_name: pytorch
license: apache-2.0
tags:
- foundation
- real_time
- android
pipeline_tag: automatic-speech-recognition
---
# DeepSpeech2: Optimized for Qualcomm Devices
DeepSpeech2 is an end-to-end automatic speech recognition (ASR) model. It uses convolutional layers for feature extraction followed by bidirectional recurrent layers and CTC for sequence-to-sequence learning.
This is based on the implementation of DeepSpeech2 found [here](https://github.com/SeanNaren/deepspeech.pytorch).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.61.0/src/qai_hub_models/models/deepspeech2) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
## Getting Started
There are two ways to deploy this model on your device:
### Option 1: Download Pre-Exported Models
Below are pre-exported model assets ready for deployment.
| Runtime | Precision | Chipset | SDK Versions | Download |
|---|---|---|---|---|
| TFLITE | float | Universal | | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/deepspeech2/releases/v0.61.0/deepspeech2-tflite-float.zip)
For more device-specific assets and performance metrics, visit **[DeepSpeech2 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/deepspeech2)**.
### Option 2: Export with Custom Configurations
Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.61.0/src/qai_hub_models/models/deepspeech2) Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations
This option is ideal if you need to customize the model beyond the default configuration provided here.
See our repository for [DeepSpeech2 on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.61.0/src/qai_hub_models/models/deepspeech2) for usage instructions.
## Model Details
**Model Type:** Model_use_case.speech_recognition
**Model Stats:**
- Input resolution: Spectrogram (800 frames x 161 features)
- Model size: 330.48MB
- Number of parameters: 94.6M
## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| DeepSpeech2 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 4052.36 ms | 188 - 199 MB | CPU
| DeepSpeech2 | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 5914.991 ms | 199 - 213 MB | CPU
| DeepSpeech2 | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 8474.079 ms | 72 - 556 MB | CPU
| DeepSpeech2 | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 18172.649 ms | 113 - 123 MB | CPU
| DeepSpeech2 | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 4083.295 ms | 62 - 69 MB | CPU
| DeepSpeech2 | TFLITE | float | Qualcomm® SA8775P | 14213.705 ms | 109 - 118 MB | CPU
| DeepSpeech2 | TFLITE | float | Qualcomm® SA8650P | 14213.705 ms | 109 - 118 MB | CPU
| DeepSpeech2 | TFLITE | float | Qualcomm® SA8255P | 14213.705 ms | 109 - 118 MB | CPU
| DeepSpeech2 | TFLITE | float | Qualcomm® QCS8450 | 5914.991 ms | 199 - 213 MB | CPU
| DeepSpeech2 | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 5277.193 ms | 67 - 551 MB | CPU
| DeepSpeech2 | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 3079.184 ms | 0 - 8 MB | CPU
| DeepSpeech2 | TFLITE | float | Qualcomm® SA7255P | 18172.649 ms | 113 - 123 MB | CPU
| DeepSpeech2 | TFLITE | float | Qualcomm® SA8295P | 9452.569 ms | 113 - 123 MB | CPU
| DeepSpeech2 | TFLITE | float | Snapdragon® 8 Elite Mobile | 3079.184 ms | 0 - 8 MB | CPU
| DeepSpeech2 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 3231.078 ms | 110 - 124 MB | CPU
## License
* The license for the original implementation of DeepSpeech2 can be found
[here](https://github.com/SeanNaren/deepspeech.pytorch/blob/master/LICENCE).
## References
* [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://arxiv.org/abs/1512.02595)
* [Source Model Implementation](https://github.com/SeanNaren/deepspeech.pytorch)
## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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