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{
"name": "46_Speech_Recognition_DeepSpeech_LibriSpeech_DL",
"query": "I'd like to develop a speech recognition system using the DeepSpeech library and the LibriSpeech dataset for me. Could you implement data loading and audio preprocessing, including noise reduction and normalization, in `src/data_loader.py`? Tune the hyperparameters such as learning rate and batch size in `src/train.py`. Please save the recognition results in `results/recognition_results.txt`. Next, create visualizations of the audio processing stages (like waveform and spectrogram) and save them as `results/figures/audio_visualization.png`. Generate a detailed report on recognition accuracy, error analysis, and suggestions for future improvements, and save it as `results/recognition_report.md`. Additionally, document the setup process for DeepSpeech, with tips for common installation issues, with [DeepSpeech documentation](https://deepspeech.readthedocs.io/en/r0.9/) as a reference. Save the final model in `models/saved_models/`. Thanks in advance!",
"tags": [
"Audio Processing"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "\"LibriSpeech\" dataset is loaded in `src/data_loader.py`.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [
0
],
"criteria": "Audio preprocessing, including noise reduction and normalization, is performed in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [
1
],
"criteria": "Hyperparameters such as learning rate and batch size are tuned in `src/train.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [
2
],
"criteria": "Save the speech recognition model in `models/saved_models/`.",
"category": "Save Trained Model",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
2
],
"criteria": "Recognition results are saved as `results/recognition_results.txt`.",
"category": "Other",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
1
],
"criteria": "Visualizations of audio processing, like waveform and spectrogram, are generated and saved as `results/figures/audio_visualization.png`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 6,
"prerequisites": [
2
],
"criteria": "A report containing recognition accuracy, error analysis, and future improvement suggestions is generated and saved as `results/recognition_report.md`.",
"category": "Performance Metrics",
"satisfied": null
}
],
"preferences": [
{
"preference_id": 0,
"criteria": "The installation process for the \"DeepSpeech\" library should be well-documented, with troubleshooting tips if the library fails to install. Refer to the [DeepSpeech documentation](https://deepspeech.readthedocs.io/en/r0.9/) for guidance.",
"satisfied": null
},
{
"preference_id": 1,
"criteria": "The visualizations should clearly depict the stages of audio processing, making it easy to interpret the effects of preprocessing.",
"satisfied": null
},
{
"preference_id": 2,
"criteria": "The report should include recommendations for alternative models or approaches if the \"DeepSpeech\" library proves challenging to implement.",
"satisfied": null
}
],
"is_kaggle_api_needed": false,
"is_training_needed": true,
"is_web_navigation_needed": true
} |