Mel Spec

A Rust implementation of mel spectrograms aligned to the results from the whisper.cpp, pytorch and librosa reference implementations and suited to streaming audio.

Usage

To require the libary's main features:

use mel_spec::prelude::*

Mel filterbank that has parity with librosa:

Mel filterbanks, within 1.0e-7 of librosa and identical to whisper GGML model-embedded filters.

        let file_path = "./testdata/mel_filters.npz";
        let f = File::open(file_path).unwrap();
        let mut npz = NpzReader::new(f).unwrap();
        let filters: Array2<f32> = npz.by_index(0).unwrap();
        let want: Array2<f64> = filters.mapv(|x| f64::from(x));
        let sampling_rate = 16000.0;
        let fft_size = 400;
        let n_mels = 80;
        let hkt = false;
        let norm = true;
        let got = mel(sampling_rate, fft_size, n_mels, hkt, norm);
        assert_eq!(got.shape(), vec![80, 201]);
        for i in 0..80 {
            assert_nearby!(got.row(i), want.row(i), 1.0e-7);
        }

Spectrogam using Short Time Fourier Transform

STFT with overlap-and-save that has parity with pytorch and whisper.cpp.

The implementation is suitable for processing streaming audio and will accumulate the correct amount of data before returning fft results.

        let fft_size = 8;
        let hop_size = 4;
        let mut spectrogram = Spectrogram::new(fft_size, hop_size);

        // Add PCM audio samples
        let frames: Vec<f32> = vec![1.0, 2.0, 3.0];
        if let Some(fft_frame) = spectrogram.add(&frames) {
            // use fft result
        }

STFT Spectrogam to Mel Spectrogram

MelSpectrogram applies a pre-computed filerbank to an FFT result. Results are identical to whisper.cpp and whisper.py

        let fft_size = 400;
        let sampling_rate = 16000.0;
        let n_mels = 80;
        let mut mel = MelSpectrogram::new(fft_size, sampling_rate, n_mels);
        // Example input data for the FFT
        let fft_input = Array1::from(vec![Complex::new(1.0, 0.0); fft_size]);
        // Add the FFT data to the MelSpectrogram
        let mel_spec = stage.add(fft_input);

Creating Mel Spectrograms from Audio.

The library includes basic audio helpder and a pipeline for processing PCM audio and creating Mel spectrograms that can be sent to whisper.cpp.

It also has voice activity detection that uses edge detection (which might be a novel approach) to identify word/speech boundaries in real- time.

        // load the whisper jfk sample
        let file_path = "../testdata/jfk_f32le.wav";
        let file = File::open(&file_path).unwrap();
        let data = parse_wav(file).unwrap();
        let samples = deinterleave_vecs_f32(&data.data, 1);

        let fft_size = 400;
        let hop_size = 160;
        let n_mels = 80;
        let sampling_rate = 16000.0;

        let mel_settings = MelConfig::new(fft_size, hop_size, n_mels, sampling_rate);
        let vad_settings = DetectionSettings::new(1.0, 10, 5, 0, 100);

        let config = PipelineConfig::new(mel_settings, Some(vad_settings));

        let mut pl = Pipeline::new(config);

        let handles = pl.start();

        // chunk size can be anything, 88 is random
        for chunk in samples[0].chunks(88) {
            let _ = pl.send_pcm(chunk);
        }

        pl.close_ingress();

        while let Ok((_, mel_spectrogram)) = pl.rx().recv() {
          // do something with spectrogram
        }

Saving Mel Spectrograms to file

Mel spectrograms can be saved in Tga format - an uncompressed image format supported by OSX and Windows.

As these images directly encode quantized mel spectrogram data they represent a "photographic negative" of audio data that whisper.cpp can develop and print without the need for direct audio input.

tga files are used in lieu of actual audio for most of the library tests. These files are lossless in Speech-to-Text terms, they encode all the information that is available in the model's view of raw audio and will produce identical results.

Note that spectrograms must have an even number of columns in the time domain, otherwise Whisper will hallucinate. the library takes care of this if using the core methods.

     let file_path = "../testdata/jfk_full_speech_chunk0_golden.tga";
     let dequantized_mel = load_tga_8bit(file_path).unwrap();
     // dequantized_mel can be sent straight to whisper.cpp

❯ ffmpeg -hide_banner -loglevel error -i ~/Downloads/JFKWHA-001-AU_WR.mp3 -f f32le -ar 16000 -acodec pcm_f32le -ac 1 pipe:1  | ./target/debug/tga_whisper -t ../../doc/cutsec_46997.tga
...
whisper_init_state: Core ML model loaded
Got 1
 the quest for peace.

the quest for peace.

Voice Activity Detection

I had the idea of using the Sobel operator for this as speech in Mel spectrograms is characterised by clear gradients.

The general idea is to outline structure in the spectrogram and then find vertical gaps that are suitable for cutting - to allow passing new spectrograms to the model in near real-time.

See the README in the main repo for more details.