axonml_audio/
lib.rs

1//! Axonml Audio - Audio Processing Utilities
2//!
3//! This crate provides audio processing functionality for the Axonml ML framework:
4//!
5//! - **Transforms**: Resampling, spectrograms, MFCC, pitch shifting, noise addition
6//! - **Datasets**: Audio classification, command recognition, music genre, speaker ID
7//!
8//! # Example
9//!
10//! ```ignore
11//! use axonml_audio::prelude::*;
12//!
13//! // Create a synthetic command dataset
14//! let dataset = SyntheticCommandDataset::small();
15//!
16//! // Apply mel spectrogram transform
17//! let mel = MelSpectrogram::new(16000);
18//! let (waveform, label) = dataset.get(0).unwrap();
19//! let spectrogram = mel.apply(&waveform);
20//! ```
21//!
22//! @version 0.1.0
23//! @author `AutomataNexus` Development Team
24
25#![warn(missing_docs)]
26#![warn(clippy::all)]
27#![warn(clippy::pedantic)]
28// ML/tensor-specific allowances
29#![allow(clippy::cast_possible_truncation)]
30#![allow(clippy::cast_sign_loss)]
31#![allow(clippy::cast_precision_loss)]
32#![allow(clippy::cast_possible_wrap)]
33#![allow(clippy::missing_errors_doc)]
34#![allow(clippy::missing_panics_doc)]
35#![allow(clippy::must_use_candidate)]
36#![allow(clippy::module_name_repetitions)]
37#![allow(clippy::similar_names)]
38#![allow(clippy::many_single_char_names)]
39#![allow(clippy::too_many_arguments)]
40#![allow(clippy::doc_markdown)]
41#![allow(clippy::cast_lossless)]
42#![allow(clippy::needless_pass_by_value)]
43#![allow(clippy::redundant_closure_for_method_calls)]
44#![allow(clippy::uninlined_format_args)]
45#![allow(clippy::ptr_arg)]
46#![allow(clippy::return_self_not_must_use)]
47#![allow(clippy::not_unsafe_ptr_arg_deref)]
48#![allow(clippy::items_after_statements)]
49#![allow(clippy::unreadable_literal)]
50#![allow(clippy::if_same_then_else)]
51#![allow(clippy::needless_range_loop)]
52#![allow(clippy::trivially_copy_pass_by_ref)]
53#![allow(clippy::unnecessary_wraps)]
54#![allow(clippy::match_same_arms)]
55#![allow(clippy::unused_self)]
56#![allow(clippy::too_many_lines)]
57#![allow(clippy::single_match_else)]
58#![allow(clippy::fn_params_excessive_bools)]
59#![allow(clippy::struct_excessive_bools)]
60#![allow(clippy::format_push_string)]
61#![allow(clippy::erasing_op)]
62#![allow(clippy::type_repetition_in_bounds)]
63#![allow(clippy::iter_without_into_iter)]
64#![allow(clippy::should_implement_trait)]
65#![allow(clippy::use_debug)]
66#![allow(clippy::case_sensitive_file_extension_comparisons)]
67#![allow(clippy::large_enum_variant)]
68#![allow(clippy::panic)]
69#![allow(clippy::struct_field_names)]
70#![allow(clippy::missing_fields_in_debug)]
71#![allow(clippy::upper_case_acronyms)]
72#![allow(clippy::assigning_clones)]
73#![allow(clippy::option_if_let_else)]
74#![allow(clippy::manual_let_else)]
75#![allow(clippy::explicit_iter_loop)]
76#![allow(clippy::default_trait_access)]
77#![allow(clippy::only_used_in_recursion)]
78#![allow(clippy::manual_clamp)]
79#![allow(clippy::ref_option)]
80#![allow(clippy::multiple_bound_locations)]
81#![allow(clippy::comparison_chain)]
82#![allow(clippy::manual_assert)]
83#![allow(clippy::unnecessary_debug_formatting)]
84
85pub mod datasets;
86pub mod transforms;
87
88// =============================================================================
89// Re-exports
90// =============================================================================
91
92pub use transforms::{
93    AddNoise, MelSpectrogram, NormalizeAudio, PitchShift, Resample, TimeStretch, TrimSilence, MFCC,
94};
95
96pub use datasets::{
97    AudioClassificationDataset, AudioSeq2SeqDataset, SyntheticCommandDataset,
98    SyntheticMusicDataset, SyntheticSpeakerDataset,
99};
100
101// =============================================================================
102// Prelude
103// =============================================================================
104
105/// Common imports for audio processing.
106pub mod prelude {
107    pub use crate::{
108        AddNoise,
109        // Datasets
110        AudioClassificationDataset,
111        AudioSeq2SeqDataset,
112        MelSpectrogram,
113        NormalizeAudio,
114        PitchShift,
115        // Transforms
116        Resample,
117        SyntheticCommandDataset,
118        SyntheticMusicDataset,
119        SyntheticSpeakerDataset,
120        TimeStretch,
121        TrimSilence,
122        MFCC,
123    };
124
125    pub use axonml_data::{DataLoader, Dataset, Transform};
126    pub use axonml_tensor::Tensor;
127}
128
129// =============================================================================
130// Tests
131// =============================================================================
132
133#[cfg(test)]
134mod tests {
135    use super::*;
136    use axonml_data::{Dataset, Transform};
137
138    #[test]
139    fn test_transform_on_dataset() {
140        let dataset = SyntheticCommandDataset::small();
141        let mel = MelSpectrogram::with_params(16000, 512, 256, 40);
142
143        let (waveform, _label) = dataset.get(0).unwrap();
144        let spectrogram = mel.apply(&waveform);
145
146        assert_eq!(spectrogram.shape()[0], 40);
147        assert!(spectrogram.shape()[1] > 0);
148    }
149
150    #[test]
151    fn test_mfcc_on_dataset() {
152        let dataset = SyntheticCommandDataset::small();
153        let mfcc = MFCC::new(16000, 13);
154
155        let (waveform, _) = dataset.get(0).unwrap();
156        let coeffs = mfcc.apply(&waveform);
157
158        assert_eq!(coeffs.shape()[0], 13);
159    }
160
161    #[test]
162    fn test_resample_on_dataset() {
163        let dataset = SyntheticCommandDataset::new(10, 22050, 0.5, 5);
164        let resample = Resample::new(22050, 16000);
165
166        let (waveform, _) = dataset.get(0).unwrap();
167        let resampled = resample.apply(&waveform);
168
169        // 0.5s at 22050Hz = 11025 samples
170        // 0.5s at 16000Hz = 8000 samples
171        assert_eq!(waveform.shape()[0], 11025);
172        assert_eq!(resampled.shape()[0], 8000);
173    }
174
175    #[test]
176    fn test_noise_augmentation() {
177        let dataset = SyntheticMusicDataset::small();
178        let add_noise = AddNoise::new(30.0); // High SNR for minimal noise
179
180        let (waveform, _) = dataset.get(0).unwrap();
181        let noisy = add_noise.apply(&waveform);
182
183        assert_eq!(noisy.shape(), waveform.shape());
184    }
185
186    #[test]
187    fn test_normalize_audio() {
188        let dataset = SyntheticSpeakerDataset::small();
189        let normalize = NormalizeAudio::new();
190
191        let (waveform, _) = dataset.get(0).unwrap();
192        let normalized = normalize.apply(&waveform);
193
194        let max_val = normalized
195            .to_vec()
196            .iter()
197            .map(|x| x.abs())
198            .fold(0.0f32, f32::max);
199        assert!((max_val - 1.0).abs() < 0.01);
200    }
201
202    #[test]
203    fn test_pipeline() {
204        let dataset = SyntheticCommandDataset::small();
205
206        // Create a processing pipeline
207        let resample = Resample::new(16000, 8000);
208        let normalize = NormalizeAudio::new();
209        let mel = MelSpectrogram::with_params(8000, 256, 128, 40);
210
211        let (waveform, _) = dataset.get(0).unwrap();
212
213        // Apply pipeline
214        let resampled = resample.apply(&waveform);
215        let normalized = normalize.apply(&resampled);
216        let spectrogram = mel.apply(&normalized);
217
218        assert_eq!(spectrogram.shape()[0], 40);
219    }
220
221    #[test]
222    fn test_time_stretch_preserves_audio_characteristics() {
223        let dataset = SyntheticMusicDataset::small();
224        let stretch = TimeStretch::new(1.0); // No change
225
226        let (waveform, _) = dataset.get(0).unwrap();
227        let stretched = stretch.apply(&waveform);
228
229        // Should be approximately the same length
230        assert_eq!(stretched.shape()[0], waveform.shape()[0]);
231    }
232
233    #[test]
234    fn test_pitch_shift() {
235        let dataset = SyntheticCommandDataset::small();
236        let shift = PitchShift::new(0.0); // No shift
237
238        let (waveform, _) = dataset.get(0).unwrap();
239        let shifted = shift.apply(&waveform);
240
241        assert_eq!(shifted.shape()[0], waveform.shape()[0]);
242    }
243
244    #[test]
245    fn test_dataset_with_dataloader() {
246        use axonml_data::DataLoader;
247
248        let dataset = SyntheticCommandDataset::small();
249        let loader = DataLoader::new(dataset, 16);
250
251        let mut batch_count = 0;
252        for batch in loader.iter().take(3) {
253            assert_eq!(batch.data.shape()[0], 16);
254            batch_count += 1;
255        }
256        assert_eq!(batch_count, 3);
257    }
258}
axonml_audio/lib.rs

axonml_audio/
lib.rs
