osobhandClaude Opus 4.7
a5cedfb46a
8-gen bench (4 emotions × 2 corpora) at seed=42 against firdhokk Whisper-LV3: target RAVDESS CREMA-D happy happy (0.999) ✓ happy (0.999) ✓ angry neutral (0.92) sad (0.99) fearful happy (0.998) fearful (0.984) ✓ sad angry (0.99) fearful (0.99) CREMA-D 2/4 vs RAVDESS 1/4. Larger / more naturalistic corpus produces more class-pure fearful direction. Neither corpus solves angry or sad — recipe shifts into 'vague expressivity' rather than class-specific corners. Practical: prefer CREMA-D when available; A/B both per emotion if class precision matters. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
90 lines
3.0 KiB
Rust
90 lines
3.0 KiB
Rust
//! Unit tests for the weight-free code paths: pure math, formatting, and the
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//! audio I/O WAV round-trip. These run on every `cargo test` — no HF auth or
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//! model downloads required.
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use approx::assert_relative_eq;
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use rtx_csm::{
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audio_io::{TARGET_SAMPLE_RATE, load_mono_24k, write_wav_24k_mono},
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config::ModelConfig,
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util::{all_zero_codebooks, rms},
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};
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#[test]
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fn rms_zero_on_identical_signals() {
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let a = vec![0.1, -0.2, 0.3, -0.4, 0.5];
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assert_relative_eq!(rms(&a, &a), 0.0, epsilon = 1e-6);
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}
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#[test]
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fn rms_matches_hand_calc() {
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let a = vec![0.0, 1.0, 2.0, 3.0];
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let b = vec![0.0, 0.0, 0.0, 0.0];
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// sum_sq = 0 + 1 + 4 + 9 = 14, n=4, rms = sqrt(14/4) ≈ 1.8708286
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assert_relative_eq!(rms(&a, &b), (14.0f32 / 4.0).sqrt(), epsilon = 1e-5);
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}
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#[test]
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fn all_zero_codebooks_true_only_on_all_zero() {
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use candle_core::{Device, Tensor};
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let dev = Device::Cpu;
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let zeros = Tensor::zeros((1, 32), candle_core::DType::I64, &dev).unwrap();
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assert!(all_zero_codebooks(&zeros).unwrap());
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// One non-zero → false.
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let mut v = vec![0i64; 32];
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v[7] = 42;
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let nonzero = Tensor::from_vec(v, (1, 32), &dev).unwrap();
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assert!(!all_zero_codebooks(&nonzero).unwrap());
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}
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#[test]
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fn speaker_formatting_brackets_id() {
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use rtx_csm::tokenizer::CsmTokenizer;
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assert_eq!(CsmTokenizer::format_segment(0, "hello"), "[0]hello");
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assert_eq!(CsmTokenizer::format_segment(1, "world"), "[1]world");
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assert_eq!(CsmTokenizer::format_segment(0, ""), "[0]");
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}
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#[test]
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fn config_csm_1b_defaults() {
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let cfg = ModelConfig::csm_1b();
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assert_eq!(cfg.text_vocab_size, 128_256);
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assert_eq!(cfg.audio_vocab_size, 2051);
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assert_eq!(cfg.audio_num_codebooks, 32);
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assert_eq!(cfg.sample_rate, 24_000);
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assert_eq!(cfg.frame_rate_hz, 12.5);
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assert_relative_eq!(cfg.frame_duration_ms(), 80.0, epsilon = 1e-6);
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}
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#[test]
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fn wav_write_read_roundtrip() {
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let tmp = tempfile::NamedTempFile::with_suffix(".wav").unwrap();
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// Synth a 0.1-second sine wave at 440 Hz so the test file is ~4.8 KB.
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let n = (TARGET_SAMPLE_RATE as f32 * 0.1) as usize;
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let input: Vec<f32> = (0..n)
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.map(|i| {
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0.5 * (2.0 * std::f32::consts::PI * 440.0 * i as f32 / TARGET_SAMPLE_RATE as f32).sin()
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})
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.collect();
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write_wav_24k_mono(tmp.path(), &input).unwrap();
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let read_back = load_mono_24k(tmp.path()).unwrap();
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// hound writes 16-bit PCM so we lose some precision; expect same length and
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// small per-sample error.
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assert_eq!(read_back.len(), input.len());
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let err = rms(&input, &read_back);
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assert!(err < 1e-3, "wav round-trip rms {err} too high");
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}
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#[test]
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fn empty_prompt_has_shape_one_zero_cb_plus_one() {
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use candle_core::{DType, Device};
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// Directly validate that a zero-length audio tensor has the right shape —
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// catches regressions in the empty-audio branch of prompt.rs.
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let dev = Device::Cpu;
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let cb = 32;
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let t = candle_core::Tensor::zeros((1, 0, cb + 1), DType::U32, &dev).unwrap();
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assert_eq!(t.dims(), &[1, 0, 33]);
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}
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