Class: Kiribi::Gemma4::E2B::AudioEncoder
- Inherits:
-
Object
- Object
- Kiribi::Gemma4::E2B::AudioEncoder
- Defined in:
- lib/kiribi/gemma4/e2b/audio_encoder.rb
Instance Method Summary collapse
-
#encode(pcm_samples) ⇒ Object
pcm_samples: 16kHz mono float32 PCM サンプル配列またはバイナリ文字列 audio_features 配列を返す.
-
#initialize ⇒ AudioEncoder
constructor
A new instance of AudioEncoder.
Constructor Details
#initialize ⇒ AudioEncoder
Returns a new instance of AudioEncoder.
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# File 'lib/kiribi/gemma4/e2b/audio_encoder.rb', line 11 def initialize @model = OnnxRuntime::Model.new(AUDIO_ENCODER_FILEPATH) end |
Instance Method Details
#encode(pcm_samples) ⇒ Object
pcm_samples: 16kHz mono float32 PCM サンプル配列またはバイナリ文字列audio_features 配列を返す
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# File 'lib/kiribi/gemma4/e2b/audio_encoder.rb', line 17 def encode(pcm_samples) pcm = pcm_samples.is_a?(String) ? pcm_samples.unpack("e*") : pcm_samples frame_length = 320 hop_length = 160 fft_length = 512 num_mels = 128 mel_floor = 0.001 window = Array.new(frame_length) { 0.5 - 0.5 * Math.cos(2.0 * Math::PI * it / frame_length) } mel_filters = build_mel_filterbank(fft_length / 2 + 1, num_mels, 0.0, 8000.0, 16_000) pad_left = frame_length / 2 padded = Array.new(pad_left, 0.0) + pcm mask_raw = Array.new(pad_left, false) + Array.new(pcm.length, true) frame_size = frame_length + 1 num_frames = (padded.length - frame_size) / hop_length + 1 input_features = [] input_features_mask = [] num_frames.times do |fi| start = fi * hop_length windowed = frame_length.times.map { padded[start + it] * window[it] } mag = rfft_magnitude(windowed, fft_length) mel = num_mels.times.map do |m| sum = 0.0 mag.each_with_index { |v, i| sum += v * mel_filters[i][m] } Math.log(sum + mel_floor) end end_idx = fi * hop_length + frame_size - 1 valid = end_idx < mask_raw.length && mask_raw[end_idx] input_features << (valid ? mel : Array.new(num_mels, 0.0)) input_features_mask << valid end # pad_to_multiple_of 128 padded_frames = ((input_features.length + 127) / 128) * 128 while input_features.length < padded_frames input_features << Array.new(num_mels, 0.0) input_features_mask << false end @model.predict({ "input_features" => [input_features], "input_features_mask" => [input_features_mask], })["audio_features"] end |