Duplicate from FluidInference/phonon-2-coreml

Co-authored-by: Alex Weng <alexwengg@users.noreply.huggingface.co>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
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Decoder.mlmodelc/model.mil

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
+{
+    func main<ios17>(tensor<fp32, [2, 1, 640]> c_in, tensor<fp32, [2, 1, 640]> h_in, tensor<int32, [1]> target_length, tensor<int32, [1, 1]> targets) {
+            tensor<int32, []> y_batch_dims_0 = const()[name = tensor<string, []>("y_batch_dims_0"), val = tensor<int32, []>(0)];
+            tensor<bool, []> y_validate_indices_0 = const()[name = tensor<string, []>("y_validate_indices_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [8193, 640]> module_prediction_embed_weight_to_fp16 = const()[name = tensor<string, []>("module_prediction_embed_weight_to_fp16"), val = tensor<fp16, [8193, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<string, []> targets_to_int16_dtype_0 = const()[name = tensor<string, []>("targets_to_int16_dtype_0"), val = tensor<string, []>("int16")];
+            tensor<string, []> cast_1_dtype_0 = const()[name = tensor<string, []>("cast_1_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, []> greater_equal_0_y_0 = const()[name = tensor<string, []>("greater_equal_0_y_0"), val = tensor<int32, []>(0)];
+            tensor<int16, [1, 1]> targets_to_int16 = cast(dtype = targets_to_int16_dtype_0, x = targets)[name = tensor<string, []>("cast_9")];
+            tensor<int32, [1, 1]> cast_1 = cast(dtype = cast_1_dtype_0, x = targets_to_int16)[name = tensor<string, []>("cast_8")];
+            tensor<bool, [1, 1]> greater_equal_0 = greater_equal(x = cast_1, y = greater_equal_0_y_0)[name = tensor<string, []>("greater_equal_0")];
+            tensor<int32, []> slice_by_index_0 = const()[name = tensor<string, []>("slice_by_index_0"), val = tensor<int32, []>(8193)];
+            tensor<int32, [1, 1]> add_2 = add(x = cast_1, y = slice_by_index_0)[name = tensor<string, []>("add_2")];
+            tensor<int32, [1, 1]> select_0 = select(a = cast_1, b = add_2, cond = greater_equal_0)[name = tensor<string, []>("select_0")];
+            tensor<int32, []> y_cast_fp16_cast_uint16_axis_0 = const()[name = tensor<string, []>("y_cast_fp16_cast_uint16_axis_0"), val = tensor<int32, []>(0)];
+            tensor<string, []> select_0_to_int16_dtype_0 = const()[name = tensor<string, []>("select_0_to_int16_dtype_0"), val = tensor<string, []>("int16")];
+            tensor<int16, [1, 1]> select_0_to_int16 = cast(dtype = select_0_to_int16_dtype_0, x = select_0)[name = tensor<string, []>("cast_7")];
+            tensor<fp16, [1, 1, 640]> y_cast_fp16_cast_uint16_cast_uint16 = gather(axis = y_cast_fp16_cast_uint16_axis_0, batch_dims = y_batch_dims_0, indices = select_0_to_int16, validate_indices = y_validate_indices_0, x = module_prediction_embed_weight_to_fp16)[name = tensor<string, []>("y_cast_fp16_cast_uint16_cast_uint16")];
+            tensor<int32, [3]> input_3_perm_0 = const()[name = tensor<string, []>("input_3_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<int32, []> split_0_num_splits_0 = const()[name = tensor<string, []>("split_0_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> split_0_axis_0 = const()[name = tensor<string, []>("split_0_axis_0"), val = tensor<int32, []>(0)];
+            tensor<string, []> h_in_to_fp16_dtype_0 = const()[name = tensor<string, []>("h_in_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [2, 1, 640]> h_in_to_fp16 = cast(dtype = h_in_to_fp16_dtype_0, x = h_in)[name = tensor<string, []>("cast_6")];
+            tensor<fp16, [1, 1, 640]> split_0_cast_fp16_0, tensor<fp16, [1, 1, 640]> split_0_cast_fp16_1 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = h_in_to_fp16)[name = tensor<string, []>("split_0_cast_fp16")];
+            tensor<int32, []> split_1_num_splits_0 = const()[name = tensor<string, []>("split_1_num_splits_0"), val = tensor<int32, []>(2)];
+            tensor<int32, []> split_1_axis_0 = const()[name = tensor<string, []>("split_1_axis_0"), val = tensor<int32, []>(0)];
+            tensor<string, []> c_in_to_fp16_dtype_0 = const()[name = tensor<string, []>("c_in_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [2, 1, 640]> c_in_to_fp16 = cast(dtype = c_in_to_fp16_dtype_0, x = c_in)[name = tensor<string, []>("cast_5")];
+            tensor<fp16, [1, 1, 640]> split_1_cast_fp16_0, tensor<fp16, [1, 1, 640]> split_1_cast_fp16_1 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = c_in_to_fp16)[name = tensor<string, []>("split_1_cast_fp16")];
+            tensor<int32, [1]> input_lstm_layer_0_lstm_h0_squeeze_axes_0 = const()[name = tensor<string, []>("input_lstm_layer_0_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_lstm_layer_0_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_0)[name = tensor<string, []>("input_lstm_layer_0_lstm_h0_squeeze_cast_fp16")];
+            tensor<int32, [1]> input_lstm_layer_0_lstm_c0_squeeze_axes_0 = const()[name = tensor<string, []>("input_lstm_layer_0_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_lstm_layer_0_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_layer_0_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_0)[name = tensor<string, []>("input_lstm_layer_0_lstm_c0_squeeze_cast_fp16")];
+            tensor<string, []> input_lstm_layer_0_direction_0 = const()[name = tensor<string, []>("input_lstm_layer_0_direction_0"), val = tensor<string, []>("forward")];
+            tensor<bool, []> input_lstm_layer_0_output_sequence_0 = const()[name = tensor<string, []>("input_lstm_layer_0_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_lstm_layer_0_recurrent_activation_0 = const()[name = tensor<string, []>("input_lstm_layer_0_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_lstm_layer_0_cell_activation_0 = const()[name = tensor<string, []>("input_lstm_layer_0_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_lstm_layer_0_activation_0 = const()[name = tensor<string, []>("input_lstm_layer_0_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp16, [2560, 640]> concat_1_to_fp16 = const()[name = tensor<string, []>("concat_1_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10487168)))];
+            tensor<fp16, [2560, 640]> concat_2_to_fp16 = const()[name = tensor<string, []>("concat_2_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13764032)))];
+            tensor<fp16, [2560]> concat_0_to_fp16 = const()[name = tensor<string, []>("concat_0_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17040896)))];
+            tensor<fp16, [1, 1, 640]> input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = y_cast_fp16_cast_uint16_cast_uint16)[name = tensor<string, []>("transpose_2")];
+            tensor<fp16, [1, 1, 640]> input_lstm_layer_0_cast_fp16_0, tensor<fp16, [1, 640]> input_lstm_layer_0_cast_fp16_1, tensor<fp16, [1, 640]> input_lstm_layer_0_cast_fp16_2 = lstm(activation = input_lstm_layer_0_activation_0, bias = concat_0_to_fp16, cell_activation = input_lstm_layer_0_cell_activation_0, direction = input_lstm_layer_0_direction_0, initial_c = input_lstm_layer_0_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_layer_0_lstm_h0_squeeze_cast_fp16, output_sequence = input_lstm_layer_0_output_sequence_0, recurrent_activation = input_lstm_layer_0_recurrent_activation_0, weight_hh = concat_2_to_fp16, weight_ih = concat_1_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("input_lstm_layer_0_cast_fp16")];
+            tensor<int32, [1]> input_lstm_h0_squeeze_axes_0 = const()[name = tensor<string, []>("input_lstm_h0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_lstm_h0_squeeze_cast_fp16 = squeeze(axes = input_lstm_h0_squeeze_axes_0, x = split_0_cast_fp16_1)[name = tensor<string, []>("input_lstm_h0_squeeze_cast_fp16")];
+            tensor<int32, [1]> input_lstm_c0_squeeze_axes_0 = const()[name = tensor<string, []>("input_lstm_c0_squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 640]> input_lstm_c0_squeeze_cast_fp16 = squeeze(axes = input_lstm_c0_squeeze_axes_0, x = split_1_cast_fp16_1)[name = tensor<string, []>("input_lstm_c0_squeeze_cast_fp16")];
+            tensor<string, []> input_direction_0 = const()[name = tensor<string, []>("input_direction_0"), val = tensor<string, []>("forward")];
+            tensor<bool, []> input_output_sequence_0 = const()[name = tensor<string, []>("input_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_recurrent_activation_0 = const()[name = tensor<string, []>("input_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_cell_activation_0 = const()[name = tensor<string, []>("input_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_activation_0 = const()[name = tensor<string, []>("input_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp16, [2560, 640]> concat_4_to_fp16 = const()[name = tensor<string, []>("concat_4_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17046080)))];
+            tensor<fp16, [2560, 640]> concat_5_to_fp16 = const()[name = tensor<string, []>("concat_5_to_fp16"), val = tensor<fp16, [2560, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(20322944)))];
+            tensor<fp16, [2560]> concat_3_to_fp16 = const()[name = tensor<string, []>("concat_3_to_fp16"), val = tensor<fp16, [2560]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23599808)))];
+            tensor<fp16, [1, 1, 640]> input_cast_fp16_0, tensor<fp16, [1, 640]> input_cast_fp16_1, tensor<fp16, [1, 640]> input_cast_fp16_2 = lstm(activation = input_activation_0, bias = concat_3_to_fp16, cell_activation = input_cell_activation_0, direction = input_direction_0, initial_c = input_lstm_c0_squeeze_cast_fp16, initial_h = input_lstm_h0_squeeze_cast_fp16, output_sequence = input_output_sequence_0, recurrent_activation = input_recurrent_activation_0, weight_hh = concat_5_to_fp16, weight_ih = concat_4_to_fp16, x = input_lstm_layer_0_cast_fp16_0)[name = tensor<string, []>("input_cast_fp16")];
+            tensor<int32, []> obj_3_axis_0 = const()[name = tensor<string, []>("obj_3_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp16, [2, 1, 640]> obj_3_cast_fp16 = stack(axis = obj_3_axis_0, values = (input_lstm_layer_0_cast_fp16_1, input_cast_fp16_1))[name = tensor<string, []>("obj_3_cast_fp16")];
+            tensor<string, []> obj_3_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("obj_3_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, []> obj_axis_0 = const()[name = tensor<string, []>("obj_axis_0"), val = tensor<int32, []>(0)];
+            tensor<fp16, [2, 1, 640]> obj_cast_fp16 = stack(axis = obj_axis_0, values = (input_lstm_layer_0_cast_fp16_2, input_cast_fp16_2))[name = tensor<string, []>("obj_cast_fp16")];
+            tensor<string, []> obj_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("obj_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, [3]> transpose_0_perm_0 = const()[name = tensor<string, []>("transpose_0_perm_0"), val = tensor<int32, [3]>([1, 2, 0])];
+            tensor<string, []> transpose_0_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("transpose_0_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp16, [1, 640, 1]> transpose_0_cast_fp16 = transpose(perm = transpose_0_perm_0, x = input_cast_fp16_0)[name = tensor<string, []>("transpose_1")];
+            tensor<fp32, [1, 640, 1]> decoder = cast(dtype = transpose_0_cast_fp16_to_fp32_dtype_0, x = transpose_0_cast_fp16)[name = tensor<string, []>("cast_2")];
+            tensor<fp32, [2, 1, 640]> c_out = cast(dtype = obj_cast_fp16_to_fp32_dtype_0, x = obj_cast_fp16)[name = tensor<string, []>("cast_3")];
+            tensor<fp32, [2, 1, 640]> h_out = cast(dtype = obj_3_cast_fp16_to_fp32_dtype_0, x = obj_3_cast_fp16)[name = tensor<string, []>("cast_4")];
+            tensor<int32, [1]> target_length_tmp = identity(x = target_length)[name = tensor<string, []>("target_length_tmp")];
+        } -> (decoder, h_out, c_out);
+}

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+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.25.2"}, {"coremltools-component-torch", "2.7.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0b1"}})]
+{
+    func main<ios17>(tensor<fp32, [1, 640, 1]> decoder_step, tensor<fp32, [1, 1024, 1]> encoder_step) {
+            tensor<int32, [3]> input_1_perm_0 = const()[name = tensor<string, []>("input_1_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> encoder_step_to_fp16_dtype_0 = const()[name = tensor<string, []>("encoder_step_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<int32, [3]> input_3_perm_0 = const()[name = tensor<string, []>("input_3_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<string, []> decoder_step_to_fp16_dtype_0 = const()[name = tensor<string, []>("decoder_step_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [640, 1024]> joint_module_enc_weight_to_fp16 = const()[name = tensor<string, []>("joint_module_enc_weight_to_fp16"), val = tensor<fp16, [640, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp16, [640]> joint_module_enc_bias_to_fp16 = const()[name = tensor<string, []>("joint_module_enc_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1310848)))];
+            tensor<fp16, [1, 1024, 1]> encoder_step_to_fp16 = cast(dtype = encoder_step_to_fp16_dtype_0, x = encoder_step)[name = tensor<string, []>("cast_5")];
+            tensor<fp16, [1, 1, 1024]> input_1_cast_fp16 = transpose(perm = input_1_perm_0, x = encoder_step_to_fp16)[name = tensor<string, []>("transpose_1")];
+            tensor<fp16, [1, 1, 640]> linear_0_cast_fp16 = linear(bias = joint_module_enc_bias_to_fp16, weight = joint_module_enc_weight_to_fp16, x = input_1_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
+            tensor<fp16, [640, 640]> joint_module_pred_weight_to_fp16 = const()[name = tensor<string, []>("joint_module_pred_weight_to_fp16"), val = tensor<fp16, [640, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1312192)))];
+            tensor<fp16, [640]> joint_module_pred_bias_to_fp16 = const()[name = tensor<string, []>("joint_module_pred_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2131456)))];
+            tensor<fp16, [1, 640, 1]> decoder_step_to_fp16 = cast(dtype = decoder_step_to_fp16_dtype_0, x = decoder_step)[name = tensor<string, []>("cast_4")];
+            tensor<fp16, [1, 1, 640]> input_3_cast_fp16 = transpose(perm = input_3_perm_0, x = decoder_step_to_fp16)[name = tensor<string, []>("transpose_0")];
+            tensor<fp16, [1, 1, 640]> linear_1_cast_fp16 = linear(bias = joint_module_pred_bias_to_fp16, weight = joint_module_pred_weight_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("linear_1_cast_fp16")];
+            tensor<int32, [1]> var_23_axes_0 = const()[name = tensor<string, []>("op_23_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 1, 1, 640]> var_23_cast_fp16 = expand_dims(axes = var_23_axes_0, x = linear_0_cast_fp16)[name = tensor<string, []>("op_23_cast_fp16")];
+            tensor<int32, [1]> var_24_axes_0 = const()[name = tensor<string, []>("op_24_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 1, 640]> var_24_cast_fp16 = expand_dims(axes = var_24_axes_0, x = linear_1_cast_fp16)[name = tensor<string, []>("op_24_cast_fp16")];
+            tensor<fp16, [1, 1, 1, 640]> input_5_cast_fp16 = add(x = var_23_cast_fp16, y = var_24_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
+            tensor<fp16, [1, 1, 1, 640]> input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
+            tensor<fp16, [8198, 640]> joint_module_joint_net_2_weight_to_fp16 = const()[name = tensor<string, []>("joint_module_joint_net_2_weight_to_fp16"), val = tensor<fp16, [8198, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2132800)))];
+            tensor<fp16, [8198]> joint_module_joint_net_2_bias_to_fp16 = const()[name = tensor<string, []>("joint_module_joint_net_2_bias_to_fp16"), val = tensor<fp16, [8198]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12626304)))];
+            tensor<fp16, [1, 1, 1, 8198]> linear_2_cast_fp16 = linear(bias = joint_module_joint_net_2_bias_to_fp16, weight = joint_module_joint_net_2_weight_to_fp16, x = input_7_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")];
+            tensor<int32, [4]> token_logits_begin_0 = const()[name = tensor<string, []>("token_logits_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [4]> token_logits_end_0 = const()[name = tensor<string, []>("token_logits_end_0"), val = tensor<int32, [4]>([1, 1, 1, 8193])];
+            tensor<bool, [4]> token_logits_end_mask_0 = const()[name = tensor<string, []>("token_logits_end_mask_0"), val = tensor<bool, [4]>([true, true, true, false])];
+            tensor<fp16, [1, 1, 1, 8193]> token_logits_cast_fp16 = slice_by_index(begin = token_logits_begin_0, end = token_logits_end_0, end_mask = token_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor<string, []>("token_logits_cast_fp16")];
+            tensor<int32, [4]> duration_logits_begin_0 = const()[name = tensor<string, []>("duration_logits_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 8193])];
+            tensor<int32, [4]> duration_logits_end_0 = const()[name = tensor<string, []>("duration_logits_end_0"), val = tensor<int32, [4]>([1, 1, 1, 8198])];
+            tensor<bool, [4]> duration_logits_end_mask_0 = const()[name = tensor<string, []>("duration_logits_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
+            tensor<fp16, [1, 1, 1, 5]> duration_logits_cast_fp16 = slice_by_index(begin = duration_logits_begin_0, end = duration_logits_end_0, end_mask = duration_logits_end_mask_0, x = linear_2_cast_fp16)[name = tensor<string, []>("duration_logits_cast_fp16")];
+            tensor<int32, []> var_43_axis_0 = const()[name = tensor<string, []>("op_43_axis_0"), val = tensor<int32, []>(-1)];
+            tensor<bool, []> var_43_keep_dims_0 = const()[name = tensor<string, []>("op_43_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<string, []> var_43_output_dtype_0 = const()[name = tensor<string, []>("op_43_output_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1, 1, 1]> token_id = reduce_argmax(axis = var_43_axis_0, keep_dims = var_43_keep_dims_0, output_dtype = var_43_output_dtype_0, x = token_logits_cast_fp16)[name = tensor<string, []>("op_43_cast_fp16")];
+            tensor<int32, []> var_49 = const()[name = tensor<string, []>("op_49"), val = tensor<int32, []>(-1)];
+            tensor<fp16, [1, 1, 1, 8193]> token_probs_all_cast_fp16 = softmax(axis = var_49, x = token_logits_cast_fp16)[name = tensor<string, []>("token_probs_all_cast_fp16")];
+            tensor<int32, [1]> var_58_axes_0 = const()[name = tensor<string, []>("op_58_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<int32, [1, 1, 1, 1]> var_58 = expand_dims(axes = var_58_axes_0, x = token_id)[name = tensor<string, []>("op_58")];
+            tensor<int32, []> var_59 = const()[name = tensor<string, []>("op_59"), val = tensor<int32, []>(-1)];
+            tensor<bool, []> var_61_validate_indices_0 = const()[name = tensor<string, []>("op_61_validate_indices_0"), val = tensor<bool, []>(false)];
+            tensor<string, []> var_58_to_int16_dtype_0 = const()[name = tensor<string, []>("op_58_to_int16_dtype_0"), val = tensor<string, []>("int16")];
+            tensor<int16, [1, 1, 1, 1]> var_58_to_int16 = cast(dtype = var_58_to_int16_dtype_0, x = var_58)[name = tensor<string, []>("cast_3")];
+            tensor<fp16, [1, 1, 1, 1]> var_61_cast_fp16_cast_int16 = gather_along_axis(axis = var_59, indices = var_58_to_int16, validate_indices = var_61_validate_indices_0, x = token_probs_all_cast_fp16)[name = tensor<string, []>("op_61_cast_fp16_cast_int16")];
+            tensor<int32, [1]> var_63_axes_0 = const()[name = tensor<string, []>("op_63_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<fp16, [1, 1, 1]> var_63_cast_fp16 = squeeze(axes = var_63_axes_0, x = var_61_cast_fp16_cast_int16)[name = tensor<string, []>("op_63_cast_fp16")];
+            tensor<string, []> var_63_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_63_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<int32, []> var_66_axis_0 = const()[name = tensor<string, []>("op_66_axis_0"), val = tensor<int32, []>(-1)];
+            tensor<bool, []> var_66_keep_dims_0 = const()[name = tensor<string, []>("op_66_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<string, []> var_66_output_dtype_0 = const()[name = tensor<string, []>("op_66_output_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [1, 1, 1]> duration = reduce_argmax(axis = var_66_axis_0, keep_dims = var_66_keep_dims_0, output_dtype = var_66_output_dtype_0, x = duration_logits_cast_fp16)[name = tensor<string, []>("op_66_cast_fp16")];
+            tensor<int32, []> var_72 = const()[name = tensor<string, []>("op_72"), val = tensor<int32, []>(64)];
+            tensor<int32, []> var_76_axis_0 = const()[name = tensor<string, []>("op_76_axis_0"), val = tensor<int32, []>(-1)];
+            tensor<bool, []> var_76_ascending_0 = const()[name = tensor<string, []>("op_76_ascending_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> var_76_sort_0 = const()[name = tensor<string, []>("op_76_sort_0"), val = tensor<bool, []>(true)];
+            tensor<bool, []> var_76_return_indices_0 = const()[name = tensor<string, []>("op_76_return_indices_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> var_76_cast_fp16_cast_int16_output_indices_dtype_0 = const()[name = tensor<string, []>("op_76_cast_fp16_cast_int16_output_indices_dtype_0"), val = tensor<string, []>("uint16")];
+            tensor<fp16, [1, 1, 1, 64]> var_76_cast_fp16_cast_int16_0, tensor<uint16, [1, 1, 1, 64]> var_76_cast_fp16_cast_int16_1 = topk(ascending = var_76_ascending_0, axis = var_76_axis_0, k = var_72, output_indices_dtype = var_76_cast_fp16_cast_int16_output_indices_dtype_0, return_indices = var_76_return_indices_0, sort = var_76_sort_0, x = token_logits_cast_fp16)[name = tensor<string, []>("op_76_cast_fp16_cast_int16")];
+            tensor<string, []> var_76_cast_fp16_cast_int16_1_to_int32_dtype_0 = const()[name = tensor<string, []>("op_76_cast_fp16_cast_int16_1_to_int32_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<string, []> var_76_cast_fp16_0_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_76_cast_fp16_0_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp32, [1, 1, 1, 64]> top_k_logits = cast(dtype = var_76_cast_fp16_0_to_fp32_dtype_0, x = var_76_cast_fp16_cast_int16_0)[name = tensor<string, []>("cast_0")];
+            tensor<int32, [1, 1, 1, 64]> top_k_ids = cast(dtype = var_76_cast_fp16_cast_int16_1_to_int32_dtype_0, x = var_76_cast_fp16_cast_int16_1)[name = tensor<string, []>("cast_1")];
+            tensor<fp32, [1, 1, 1]> token_prob = cast(dtype = var_63_cast_fp16_to_fp32_dtype_0, x = var_63_cast_fp16)[name = tensor<string, []>("cast_2")];
+        } -> (token_id, token_prob, duration, top_k_ids, top_k_logits);
+}

JointDecisionv3.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f922e335bc57174645fd6ede581395f1ae846f7227898301b5a1a7585f4ab45
+size 12642764

LICENSE

@@ -0,0 +1,396 @@
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+Section 5 -- Disclaimer of Warranties and Limitation of Liability.
+
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+Section 6 -- Term and Termination.
+
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+  d. Sections 1, 5, 6, 7, and 8 survive termination of this Public
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+=======================================================================
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+
+Creative Commons may be contacted at creativecommons.org.
+

NOTICE

@@ -0,0 +1,58 @@
+Phonon-2
+Copyright 2026 Fermion Research
+
+This product includes software and models developed by Fermion Research.
+
+================================================================================
+BASE MODEL
+================================================================================
+
+This model is derived from NVIDIA's Parakeet TDT 0.6B v3
+(nvidia/parakeet-tdt-0.6b-v3), (c) NVIDIA Corporation, licensed under the
+Creative Commons Attribution 4.0 International licence (CC-BY-4.0).
+
+    https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3
+    https://creativecommons.org/licenses/by/4.0/
+
+Changes: the encoder's linear weights were re-trained and quantised to a
+five-value alphabet by Fermion Research; the remaining parameters were
+quantised to 6-bit integer tables. The FastConformer encoder architecture,
+the TDT decoder, the tokenizer and the feature-extractor configuration derive
+from that model and remain under CC-BY-4.0.
+
+These weights are therefore distributed under CC-BY-4.0. The Fermion code in
+this repository remains under the Apache License, Version 2.0.
+
+================================================================================
+TRAINING DATA
+================================================================================
+
+Training audio includes CHiME-6, distributed under the Creative Commons
+Attribution-ShareAlike 4.0 International licence (CC-BY-SA-4.0)
+
+    https://chimechallenge.github.io/chime6/
+    https://creativecommons.org/licenses/by-sa/4.0/
+
+and SPGISpeech, used under Kensho's public terms
+
+    https://datasets.kensho.com/datasets/spgispeech
+
+================================================================================
+RUNTIME DEPENDENCIES
+================================================================================
+
+MLX ......................... MIT License ......... https://github.com/ml-explore/mlx
+mlx-audio ................... MIT License ......... https://github.com/Blaizzy/mlx-audio
+    The Parakeet model graph (feature extraction, FastConformer encoder,
+    TDT decoder and greedy search) is mlx-audio's implementation.
+NumPy ....................... BSD-3-Clause ........ https://numpy.org
+soundfile ................... BSD-3-Clause ........ https://github.com/bastibe/python-soundfile
+    soundfile links libsndfile (LGPL-2.1), dynamically and unmodified, as
+    distributed in the standard soundfile wheel.
+
+================================================================================
+REFERENCE RUNTIME (optional)
+================================================================================
+
+PyTorch ..................... BSD-3-Clause ........ https://pytorch.org
+transformers ................ Apache-2.0 .......... https://github.com/huggingface/transformers

Preprocessor.mlmodelc/analytics/coremldata.bin

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+size 243

Preprocessor.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
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+oid sha256:dbde3f2300842c1fd51ef3ff948a0bcffe65ffd2dca10707f2509f32c1d65b1d
+size 486

Preprocessor.mlmodelc/metadata.json

@@ -0,0 +1,105 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "shortDescription" : "int8-linear quantized - preprocessor",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 128 × 1501)",
+        "shortDescription" : "",
+        "shape" : "[1, 128, 1501]",
+        "name" : "mel",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "mel_length",
+        "type" : "MultiArray"
+      }
+    ],
+    "storagePrecision" : "Int8",
+    "modelParameters" : [
+
+    ],
+    "author" : "Fluid Inference",
+    "specificationVersion" : 8,
+    "mlProgramOperationTypeHistogram" : {
+      "Ios17.reshape" : 2,
+      "Ios17.matmul" : 1,
+      "Select" : 3,
+      "Ios17.expandDims" : 8,
+      "Ios17.add" : 4,
+      "Tile" : 1,
+      "Ios17.sliceByIndex" : 3,
+      "Ios16.reduceSum" : 4,
+      "Pad" : 1,
+      "Ios17.log" : 1,
+      "Ios16.constexprAffineDequantize" : 4,
+      "Ios17.less" : 1,
+      "Ios17.sub" : 4,
+      "Ios17.conv" : 2,
+      "Ios17.pow" : 2,
+      "Ios17.cast" : 5,
+      "Stack" : 1,
+      "Ios17.concat" : 1,
+      "Ios17.floorDiv" : 1,
+      "Ios17.realDiv" : 3,
+      "Ios17.sqrt" : 1,
+      "Ios17.greaterEqual" : 1,
+      "Ios17.mul" : 1
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "14.0",
+      "tvOS" : "17.0",
+      "visionOS" : "1.0",
+      "watchOS" : "10.0",
+      "iOS" : "17.0",
+      "macCatalyst" : "17.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 240000)",
+        "shortDescription" : "",
+        "shape" : "[1, 240000]",
+        "name" : "audio_signal",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Int32",
+        "formattedType" : "MultiArray (Int32 1)",
+        "shortDescription" : "",
+        "shape" : "[1]",
+        "name" : "audio_length",
+        "type" : "MultiArray"
+      }
+    ],
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.conversion_date" : "2025-09-19",
+      "com.github.apple.coremltools.source" : "torch==2.7.0",
+      "com.github.apple.coremltools.version" : "9.0b1",
+      "com.github.apple.coremltools.source_dialect" : "TorchScript"
+    },
+    "generatedClassName" : "parakeet_preprocessor",
+    "method" : "predict"
+  }
+]

Preprocessor.mlmodelc/model.mil

@@ -0,0 +1,125 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}})]
+{
+    func main<ios17>(tensor<int32, [1]> audio_length, tensor<fp32, [1, 240000]> audio_signal) {
+            tensor<int32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<int32, []>(1)];
+            tensor<int32, []> var_10 = const()[name = tensor<string, []>("op_10"), val = tensor<int32, []>(160)];
+            tensor<int32, []> var_34 = const()[name = tensor<string, []>("op_34"), val = tensor<int32, []>(512)];
+            tensor<int32, [1]> var_35 = add(x = audio_length, y = var_34)[name = tensor<string, []>("op_35")];
+            tensor<int32, []> var_36 = const()[name = tensor<string, []>("op_36"), val = tensor<int32, []>(512)];
+            tensor<int32, [1]> var_37 = sub(x = var_35, y = var_36)[name = tensor<string, []>("op_37")];
+            tensor<int32, [1]> floor_div_0 = floor_div(x = var_37, y = var_10)[name = tensor<string, []>("floor_div_0")];
+            tensor<string, []> var_38_to_fp16_dtype_0 = const()[name = tensor<string, []>("op_38_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, []> var_39_promoted_to_fp16 = const()[name = tensor<string, []>("op_39_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1]> floor_div_0_to_fp16 = cast(dtype = var_38_to_fp16_dtype_0, x = floor_div_0)[name = tensor<string, []>("cast_4")];
+            tensor<fp16, [1]> seq_len_1_cast_fp16 = add(x = floor_div_0_to_fp16, y = var_39_promoted_to_fp16)[name = tensor<string, []>("seq_len_1_cast_fp16")];
+            tensor<string, []> seq_len_dtype_0 = const()[name = tensor<string, []>("seq_len_dtype_0"), val = tensor<string, []>("int32")];
+            tensor<int32, [2]> var_43_begin_0 = const()[name = tensor<string, []>("op_43_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_43_end_0 = const()[name = tensor<string, []>("op_43_end_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<bool, [2]> var_43_end_mask_0 = const()[name = tensor<string, []>("op_43_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<bool, [2]> var_43_squeeze_mask_0 = const()[name = tensor<string, []>("op_43_squeeze_mask_0"), val = tensor<bool, [2]>([false, true])];
+            tensor<string, []> audio_signal_to_fp16_dtype_0 = const()[name = tensor<string, []>("audio_signal_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [1, 240000]> audio_signal_to_fp16 = cast(dtype = audio_signal_to_fp16_dtype_0, x = audio_signal)[name = tensor<string, []>("cast_3")];
+            tensor<fp16, [1]> var_43_cast_fp16 = slice_by_index(begin = var_43_begin_0, end = var_43_end_0, end_mask = var_43_end_mask_0, squeeze_mask = var_43_squeeze_mask_0, x = audio_signal_to_fp16)[name = tensor<string, []>("op_43_cast_fp16")];
+            tensor<int32, [1]> var_44_axes_0 = const()[name = tensor<string, []>("op_44_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_44_cast_fp16 = expand_dims(axes = var_44_axes_0, x = var_43_cast_fp16)[name = tensor<string, []>("op_44_cast_fp16")];
+            tensor<int32, [2]> var_46_begin_0 = const()[name = tensor<string, []>("op_46_begin_0"), val = tensor<int32, [2]>([0, 1])];
+            tensor<int32, [2]> var_46_end_0 = const()[name = tensor<string, []>("op_46_end_0"), val = tensor<int32, [2]>([1, 240000])];
+            tensor<bool, [2]> var_46_end_mask_0 = const()[name = tensor<string, []>("op_46_end_mask_0"), val = tensor<bool, [2]>([true, true])];
+            tensor<fp16, [1, 239999]> var_46_cast_fp16 = slice_by_index(begin = var_46_begin_0, end = var_46_end_0, end_mask = var_46_end_mask_0, x = audio_signal_to_fp16)[name = tensor<string, []>("op_46_cast_fp16")];
+            tensor<int32, [2]> var_48_begin_0 = const()[name = tensor<string, []>("op_48_begin_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [2]> var_48_end_0 = const()[name = tensor<string, []>("op_48_end_0"), val = tensor<int32, [2]>([1, 239999])];
+            tensor<bool, [2]> var_48_end_mask_0 = const()[name = tensor<string, []>("op_48_end_mask_0"), val = tensor<bool, [2]>([true, false])];
+            tensor<fp16, [1, 239999]> var_48_cast_fp16 = slice_by_index(begin = var_48_begin_0, end = var_48_end_0, end_mask = var_48_end_mask_0, x = audio_signal_to_fp16)[name = tensor<string, []>("op_48_cast_fp16")];
+            tensor<fp16, []> var_49_to_fp16 = const()[name = tensor<string, []>("op_49_to_fp16"), val = tensor<fp16, []>(0x1.f0cp-1)];
+            tensor<fp16, [1, 239999]> var_50_cast_fp16 = mul(x = var_48_cast_fp16, y = var_49_to_fp16)[name = tensor<string, []>("op_50_cast_fp16")];
+            tensor<fp16, [1, 239999]> var_51_cast_fp16 = sub(x = var_46_cast_fp16, y = var_50_cast_fp16)[name = tensor<string, []>("op_51_cast_fp16")];
+            tensor<bool, []> input_1_interleave_0 = const()[name = tensor<string, []>("input_1_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 240000]> input_1_cast_fp16 = concat(axis = var_9, interleave = input_1_interleave_0, values = (var_44_cast_fp16, var_51_cast_fp16))[name = tensor<string, []>("input_1_cast_fp16")];
+            tensor<int32, [3]> var_57 = const()[name = tensor<string, []>("op_57"), val = tensor<int32, [3]>([1, 1, 240000])];
+            tensor<fp16, [1, 1, 240000]> input_3_cast_fp16 = reshape(shape = var_57, x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<int32, [6]> input_5_pad_0 = const()[name = tensor<string, []>("input_5_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 256, 256])];
+            tensor<string, []> input_5_mode_0 = const()[name = tensor<string, []>("input_5_mode_0"), val = tensor<string, []>("reflect")];
+            tensor<fp16, []> const_3_to_fp16 = const()[name = tensor<string, []>("const_3_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 1, 240512]> input_5_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = input_5_mode_0, pad = input_5_pad_0, x = input_3_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
+            tensor<int32, [2]> var_63 = const()[name = tensor<string, []>("op_63"), val = tensor<int32, [2]>([1, 240512])];
+            tensor<fp16, [1, 240512]> input_cast_fp16 = reshape(shape = var_63, x = input_5_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
+            tensor<int32, [1]> expand_dims_5 = const()[name = tensor<string, []>("expand_dims_5"), val = tensor<int32, [1]>([160])];
+            tensor<int32, [1]> expand_dims_6_axes_0 = const()[name = tensor<string, []>("expand_dims_6_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1, 240512]> expand_dims_6_cast_fp16 = expand_dims(axes = expand_dims_6_axes_0, x = input_cast_fp16)[name = tensor<string, []>("expand_dims_6_cast_fp16")];
+            tensor<string, []> conv_0_pad_type_0 = const()[name = tensor<string, []>("conv_0_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> conv_0_pad_0 = const()[name = tensor<string, []>("conv_0_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_0_dilations_0 = const()[name = tensor<string, []>("conv_0_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> conv_0_groups_0 = const()[name = tensor<string, []>("conv_0_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_3_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("expand_dims_3_to_fp16_quantized"), quantized_data = tensor<int8, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64))), scale = tensor<fp16, [257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132096))), zero_point = tensor<int8, [257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(131712)))];
+            tensor<fp16, [1, 257, 1501]> conv_0_cast_fp16 = conv(dilations = conv_0_dilations_0, groups = conv_0_groups_0, pad = conv_0_pad_0, pad_type = conv_0_pad_type_0, strides = expand_dims_5, weight = expand_dims_3_to_fp16_quantized, x = expand_dims_6_cast_fp16)[name = tensor<string, []>("conv_0_cast_fp16")];
+            tensor<string, []> conv_1_pad_type_0 = const()[name = tensor<string, []>("conv_1_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> conv_1_pad_0 = const()[name = tensor<string, []>("conv_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> conv_1_dilations_0 = const()[name = tensor<string, []>("conv_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> conv_1_groups_0 = const()[name = tensor<string, []>("conv_1_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp16, [257, 1, 512]> expand_dims_4_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("expand_dims_4_to_fp16_quantized"), quantized_data = tensor<int8, [257, 1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(132736))), scale = tensor<fp16, [257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264768))), zero_point = tensor<int8, [257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(264384)))];
+            tensor<fp16, [1, 257, 1501]> conv_1_cast_fp16 = conv(dilations = conv_1_dilations_0, groups = conv_1_groups_0, pad = conv_1_pad_0, pad_type = conv_1_pad_type_0, strides = expand_dims_5, weight = expand_dims_4_to_fp16_quantized, x = expand_dims_6_cast_fp16)[name = tensor<string, []>("conv_1_cast_fp16")];
+            tensor<int32, []> stack_0_axis_0 = const()[name = tensor<string, []>("stack_0_axis_0"), val = tensor<int32, []>(-1)];
+            tensor<fp16, [1, 257, 1501, 2]> stack_0_cast_fp16 = stack(axis = stack_0_axis_0, values = (conv_0_cast_fp16, conv_1_cast_fp16))[name = tensor<string, []>("stack_0_cast_fp16")];
+            tensor<fp16, []> var_17_promoted_to_fp16 = const()[name = tensor<string, []>("op_17_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
+            tensor<fp16, [1, 257, 1501, 2]> var_67_cast_fp16 = pow(x = stack_0_cast_fp16, y = var_17_promoted_to_fp16)[name = tensor<string, []>("op_67_cast_fp16")];
+            tensor<int32, [1]> var_69_axes_0 = const()[name = tensor<string, []>("op_69_axes_0"), val = tensor<int32, [1]>([-1])];
+            tensor<bool, []> var_69_keep_dims_0 = const()[name = tensor<string, []>("op_69_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 257, 1501]> var_69_cast_fp16 = reduce_sum(axes = var_69_axes_0, keep_dims = var_69_keep_dims_0, x = var_67_cast_fp16)[name = tensor<string, []>("op_69_cast_fp16")];
+            tensor<bool, []> x_11_transpose_x_0 = const()[name = tensor<string, []>("x_11_transpose_x_0"), val = tensor<bool, []>(false)];
+            tensor<bool, []> x_11_transpose_y_0 = const()[name = tensor<string, []>("x_11_transpose_y_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 128, 257]> const_6_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(1), name = tensor<string, []>("const_6_to_fp16_quantized"), quantized_data = tensor<int8, [1, 128, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(265408))), scale = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(298560))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(298368)))];
+            tensor<fp16, [1, 128, 1501]> x_11_cast_fp16 = matmul(transpose_x = x_11_transpose_x_0, transpose_y = x_11_transpose_y_0, x = const_6_to_fp16_quantized, y = var_69_cast_fp16)[name = tensor<string, []>("x_11_cast_fp16")];
+            tensor<fp16, []> var_76_to_fp16 = const()[name = tensor<string, []>("op_76_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
+            tensor<fp16, [1, 128, 1501]> var_77_cast_fp16 = add(x = x_11_cast_fp16, y = var_76_to_fp16)[name = tensor<string, []>("op_77_cast_fp16")];
+            tensor<fp32, []> x_13_epsilon_0 = const()[name = tensor<string, []>("x_13_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
+            tensor<fp16, [1, 128, 1501]> x_13_cast_fp16 = log(epsilon = x_13_epsilon_0, x = var_77_cast_fp16)[name = tensor<string, []>("x_13_cast_fp16")];
+            tensor<int32, [1, 1501]> var_82 = const()[name = tensor<string, []>("op_82"), val = tensor<int32, [1, 1501]>([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 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1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500]])];
+            tensor<int32, [1]> var_85_axes_0 = const()[name = tensor<string, []>("op_85_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [1]> mel_length = cast(dtype = seq_len_dtype_0, x = seq_len_1_cast_fp16)[name = tensor<string, []>("cast_2")];
+            tensor<int32, [1, 1]> var_85 = expand_dims(axes = var_85_axes_0, x = mel_length)[name = tensor<string, []>("op_85")];
+            tensor<bool, [1, 1501]> valid_mask = less(x = var_82, y = var_85)[name = tensor<string, []>("valid_mask")];
+            tensor<int32, [1]> var_87_axes_0 = const()[name = tensor<string, []>("op_87_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_87 = expand_dims(axes = var_87_axes_0, x = valid_mask)[name = tensor<string, []>("op_87")];
+            tensor<int32, [3]> var_87_after_broadcast_reps_0 = const()[name = tensor<string, []>("op_87_after_broadcast_reps_0"), val = tensor<int32, [3]>([1, 128, 1])];
+            tensor<bool, [1, 128, 1501]> var_87_after_broadcast = tile(reps = var_87_after_broadcast_reps_0, x = var_87)[name = tensor<string, []>("op_87_after_broadcast")];
+            tensor<fp16, [1, 128, 1501]> op_24_after_broadcast_to_fp16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("op_24_after_broadcast_to_fp16_quantized"), quantized_data = tensor<int8, [1, 128, 1501]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(298880))), scale = tensor<fp16, []>(0x0p+0), zero_point = tensor<int8, []>(0)];
+            tensor<fp16, [1, 128, 1501]> var_88_cast_fp16 = select(a = x_13_cast_fp16, b = op_24_after_broadcast_to_fp16_quantized, cond = var_87_after_broadcast)[name = tensor<string, []>("op_88_cast_fp16")];
+            tensor<int32, [1]> x_mean_numerator_axes_0 = const()[name = tensor<string, []>("x_mean_numerator_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<bool, []> x_mean_numerator_keep_dims_0 = const()[name = tensor<string, []>("x_mean_numerator_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 128]> x_mean_numerator_cast_fp16 = reduce_sum(axes = x_mean_numerator_axes_0, keep_dims = x_mean_numerator_keep_dims_0, x = var_88_cast_fp16)[name = tensor<string, []>("x_mean_numerator_cast_fp16")];
+            tensor<int32, [1]> x_mean_denominator_axes_0 = const()[name = tensor<string, []>("x_mean_denominator_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, []> x_mean_denominator_keep_dims_0 = const()[name = tensor<string, []>("x_mean_denominator_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<string, []> cast_2_to_fp16_dtype_0 = const()[name = tensor<string, []>("cast_2_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<fp16, [1, 1501]> valid_mask_to_fp16 = cast(dtype = cast_2_to_fp16_dtype_0, x = valid_mask)[name = tensor<string, []>("cast_1")];
+            tensor<fp16, [1]> x_mean_denominator_cast_fp16 = reduce_sum(axes = x_mean_denominator_axes_0, keep_dims = x_mean_denominator_keep_dims_0, x = valid_mask_to_fp16)[name = tensor<string, []>("x_mean_denominator_cast_fp16")];
+            tensor<int32, [1]> var_93_axes_0 = const()[name = tensor<string, []>("op_93_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp16, [1, 1]> var_93_cast_fp16 = expand_dims(axes = var_93_axes_0, x = x_mean_denominator_cast_fp16)[name = tensor<string, []>("op_93_cast_fp16")];
+            tensor<fp16, [1, 128]> x_mean_cast_fp16 = real_div(x = x_mean_numerator_cast_fp16, y = var_93_cast_fp16)[name = tensor<string, []>("x_mean_cast_fp16")];
+            tensor<int32, [1]> var_96_axes_0 = const()[name = tensor<string, []>("op_96_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 128, 1]> var_96_cast_fp16 = expand_dims(axes = var_96_axes_0, x = x_mean_cast_fp16)[name = tensor<string, []>("op_96_cast_fp16")];
+            tensor<fp16, [1, 128, 1501]> var_97_cast_fp16 = sub(x = x_13_cast_fp16, y = var_96_cast_fp16)[name = tensor<string, []>("op_97_cast_fp16")];
+            tensor<fp16, [1, 128, 1501]> var_98_cast_fp16 = select(a = var_97_cast_fp16, b = op_24_after_broadcast_to_fp16_quantized, cond = var_87_after_broadcast)[name = tensor<string, []>("op_98_cast_fp16")];
+            tensor<fp16, []> var_17_promoted_1_to_fp16 = const()[name = tensor<string, []>("op_17_promoted_1_to_fp16"), val = tensor<fp16, []>(0x1p+1)];
+            tensor<fp16, [1, 128, 1501]> var_99_cast_fp16 = pow(x = var_98_cast_fp16, y = var_17_promoted_1_to_fp16)[name = tensor<string, []>("op_99_cast_fp16")];
+            tensor<int32, [1]> var_101_axes_0 = const()[name = tensor<string, []>("op_101_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<bool, []> var_101_keep_dims_0 = const()[name = tensor<string, []>("op_101_keep_dims_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 128]> var_101_cast_fp16 = reduce_sum(axes = var_101_axes_0, keep_dims = var_101_keep_dims_0, x = var_99_cast_fp16)[name = tensor<string, []>("op_101_cast_fp16")];
+            tensor<fp16, []> var_103_to_fp16 = const()[name = tensor<string, []>("op_103_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
+            tensor<fp16, [1, 1]> var_104_cast_fp16 = sub(x = var_93_cast_fp16, y = var_103_to_fp16)[name = tensor<string, []>("op_104_cast_fp16")];
+            tensor<fp16, [1, 128]> var_105_cast_fp16 = real_div(x = var_101_cast_fp16, y = var_104_cast_fp16)[name = tensor<string, []>("op_105_cast_fp16")];
+            tensor<fp16, [1, 128]> x_std_1_cast_fp16 = sqrt(x = var_105_cast_fp16)[name = tensor<string, []>("x_std_1_cast_fp16")];
+            tensor<fp16, []> var_25_to_fp16 = const()[name = tensor<string, []>("op_25_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
+            tensor<fp16, [1, 128]> x_std_cast_fp16 = add(x = x_std_1_cast_fp16, y = var_25_to_fp16)[name = tensor<string, []>("x_std_cast_fp16")];
+            tensor<int32, [1]> var_110_axes_0 = const()[name = tensor<string, []>("op_110_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp16, [1, 128, 1]> var_110_cast_fp16 = expand_dims(axes = var_110_axes_0, x = x_std_cast_fp16)[name = tensor<string, []>("op_110_cast_fp16")];
+            tensor<fp16, [1, 128, 1501]> x_cast_fp16 = real_div(x = var_97_cast_fp16, y = var_110_cast_fp16)[name = tensor<string, []>("x_cast_fp16")];
+            tensor<bool, [1, 1501]> mask = greater_equal(x = var_82, y = var_85)[name = tensor<string, []>("mask")];
+            tensor<int32, [1]> var_119_axes_0 = const()[name = tensor<string, []>("op_119_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<bool, [1, 1, 1501]> var_119 = expand_dims(axes = var_119_axes_0, x = mask)[name = tensor<string, []>("op_119")];
+            tensor<fp16, []> var_24_to_fp16 = const()[name = tensor<string, []>("op_24_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 128, 1501]> processed_signal_cast_fp16 = select(a = var_24_to_fp16, b = x_cast_fp16, cond = var_119)[name = tensor<string, []>("processed_signal_cast_fp16")];
+            tensor<string, []> processed_signal_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("processed_signal_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp32, [1, 128, 1501]> mel = cast(dtype = processed_signal_cast_fp16_to_fp32_dtype_0, x = processed_signal_cast_fp16)[name = tensor<string, []>("cast_0")];
+        } -> (mel, mel_length);
+}

Preprocessor.mlmodelc/weights/weight.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:129b76e3aeafa8afa3ea76d995b964b145fe83700d579f6ff42c4c38fa0968ea
+size 491072

README.md

@@ -0,0 +1,112 @@
+---
+license: cc-by-4.0
+base_model: FermionResearch/Phonon-2
+base_model_relation: quantized
+language: [en]
+pipeline_tag: automatic-speech-recognition
+tags: [coreml, parakeet, tdt, phonon, low-bit, sparse, speech-recognition, apple-silicon]
+library_name: fluidaudio
+---
+
+# phonon-2-coreml
+
+Core ML build of [FermionResearch/Phonon-2](https://huggingface.co/FermionResearch/Phonon-2), Fermion Research's
+quantization-aware re-training of `nvidia/parakeet-tdt-0.6b-v3` for English, in which every encoder weight takes one of
+five learned values per output row. Same tokenizer, 15 s window and `Decoder` / `JointDecisionv3` contract as
+[parakeet-tdt-0.6b-v3-coreml](https://huggingface.co/FluidInference/parakeet-tdt-0.6b-v3-coreml); loaded in
+[FluidAudio](https://github.com/FluidInference/FluidAudio) via `AsrModelVersion.phonon2`.
+
+Every encoder here holds the checkpoint's **exact** five-value weights — as fp16 palettes (iOS 18 / macOS 15
+`constexpr_lut_to_dense`), or as a sparsity mask plus a palette over the non-zero weights (`constexpr_lut_to_sparse` +
+`constexpr_sparse_to_dense`). No re-quantization noise is added, and all five produce identical transcripts. The decoder
+and joint are re-exported from the checkpoint's int6 tables; preprocessor and vocabulary are the v3 ones.
+
+## Usage (FluidAudio, merged in [#980](https://github.com/FluidInference/FluidAudio/pull/980))
+
+```swift
+let models = try await AsrModels.downloadAndLoad(version: .phonon2)  // iOS 18+ / macOS 15+, downloads Encoder.mlmodelc
+let asr = AsrManager()
+try await asr.initialize(models: models)
+let result = try await asr.transcribe(audioFileURL)
+```
+
+```bash
+swift run fluidaudiocli transcribe audio.wav --model-version phonon2
+swift run fluidaudiocli asr-benchmark --subset test-clean --model-version phonon2
+```
+
+To use another encoder file, download the model directory, rename the chosen `Encoder_*.mlmodelc` to `Encoder.mlmodelc`
+and load it with `AsrModels.loadLocal(from:version: .phonon2, encoderComputeUnits:)`.
+
+## Encoder files
+
+One 15 s window on an M5 Pro (macOS 27); RTFx is FluidAudio `asr-benchmark` on full LibriSpeech test-clean with the
+encoder on the Neural Engine. All five are exact and give the same transcripts.
+
+| File | Size | Encoding | ANE | ANE RTFx | GPU |
+|---|---:|---|---:|---:|---|
+| **`Encoder.mlmodelc`** (default) | **321 MB** | sparse mask + 6-bit palette per 8 rows | **18.6 ms** | **159×** | 16 ms, but ~150 s load on every launch |
+| `Encoder_sparse-g4.mlmodelc` | 246 MB | sparse mask + 4-bit palette per 4 rows | 24.3 ms | 140× | same load caveat |
+| `Encoder_sparse-g1.mlmodelc` | 176 MB | sparse mask + 2-bit palette per row | 70 ms | ~70× | same load caveat |
+| `Encoder_lut6.mlmodelc` | 470 MB | dense 6-bit palette per 8 rows | 18.6 ms | 155× | 16 ms, 0.6 s load |
+| `Encoder_lut3.mlmodelc` | 253 MB | dense 3-bit palette per row | 72 ms | 70× | 16 ms, 0.7 s load |
+| v3 `Encoder.mlmodelc` (6-bit, for reference) | 445 MB | — | 23.5 ms | 149–152× | 18 ms |
+
+Two facts decide the choice. The Neural Engine's palette cost grows with the number of palettes, not their bit width,
+so 8 rows per palette is faster than v3's own encoder while per-row palettes are 3× slower. And the GPU materializes
+*sparse* weights at every load (~150 s of CPU, not cached), while the ANE compiles them once (~1 min) and caches. So:
+Neural Engine (the FluidAudio default, and the only option for iOS background work) → `Encoder.mlmodelc`; GPU →
+`Encoder_lut3.mlmodelc` (small) or `Encoder_lut6.mlmodelc` (fast everywhere); smallest possible → `Encoder_sparse-g1`.
+
+Other files: `Decoder.mlmodelc` (RNNT prediction net, fp16, iOS 17+), `JointDecisionv3.mlmodelc` (single-step joint +
+top-64, fp16, iOS 17+), `Preprocessor.mlmodelc` and `parakeet_vocab.json` (identical to v3), `NOTICE` / `LICENSE`
+(upstream attribution, CC-BY-4.0).
+
+## Accuracy vs parakeet-tdt-0.6b-v3
+
+Full LibriSpeech, FluidAudio `asr-benchmark`, M5 Pro, both models run back to back with the default (ANE) encoder. WER is
+corpus-level (total edit distance over total reference words); RTFx = total audio / total processing time.
+
+| Set (ANE) | v3 | Ultra | Phonon-2 |
+|---|---|---|---|
+| test-clean (2620 files) WER | 2.27 % | **2.13 %** | 2.47 % |
+| test-other (2939 files) WER | 4.12 % | **3.81 %** | 4.62 % |
+| test-clean RTFx | 149–152× | 151× | **159×** |
+| test-other RTFx | 138× | 142× | **146×** |
+
+v3 is the more accurate model on LibriSpeech by 0.20 / 0.50 points, reproducing the upstream card's own gap to its
+teacher (+0.20 / +0.79 under the Open ASR Leaderboard protocol, where Phonon-2 in turn beats v3 on AMI meetings and
+VoxPopuli). Phonon-2 is English only; v3 covers 25 languages. The absolute values are above the card's because
+FluidAudio decodes in 15 s windows with a simpler normalizer, which both models pay equally.
+
+Conversion fidelity: on the first 100 test-clean files the Core ML transcripts differ from a NeMo fp32 full-context
+decode of the same checkpoint by 0.34 % WER (1.83 % vs 1.79 %).
+
+
+## 60-minute long-form file (Earnings-22, four concatenated calls)
+
+`transcribe` on the 3600 s `earnings22_top4_1h.wav` (M5 Pro, default ANE encoder, best of 2–3 runs, processing time
+excludes model load). Reference = the concatenated Earnings-22 chunk transcripts, same normalizer.
+
+| Model | Processing time | RTFx | WER |
+|---|---:|---:|---:|
+| v3 | 10.9 s | 331× | 16.5 % |
+| Ultra | 7.7 s | 469× | **13.5 %** |
+| Redux | 14.2 s | 254× | 14.8 % |
+| Phonon-2 default (sparse, 321 MB) | 7.5 s | **478×** | 17.2 % |
+| Phonon-2 `Encoder_lut6` (470 MB) | 7.4 s | 486× | 17.2 % |
+| Phonon-2 `Encoder_sparse-g4` (246 MB) | 9.0 s | 399× | 17.2 % |
+| Phonon-2 `Encoder_sparse-g1` (176 MB) | 23.4 s | 154× | 17.2 % |
+| Phonon-2 `Encoder_lut3` (253 MB) | 23.6 s | 152× | 17.2 % |
+
+On conversational long-form audio Phonon-2 is the fastest model we ship (1.45× v3's throughput, on par with Ultra) but
+the least accurate of the four: Ultra and Redux both beat v3 here while Phonon-2 trails it by 0.8 points, consistent
+with the upstream card's Earnings-22 row (6.96 % vs its teacher's 5.85 %). All five Phonon-2 encoders produce the same
+transcript.
+
+## Licence and attribution
+
+The weights are a derivative of NVIDIA's parakeet-tdt-0.6b-v3 re-trained by Fermion Research and are distributed under
+CC-BY-4.0, as upstream. `NOTICE` reproduces Fermion Research's change list and training-data attribution. The encoders are
+exact re-encodings of the upstream weights (grouped-channel palettes, or a sparsity mask plus palettes over the non-zeros);
+the decoder and joint are the upstream int6 tables in fp16.

config.json

@@ -0,0 +1,39 @@
+{
+  "base_model": "FermionResearch/Phonon-2",
+  "original_base_model": "nvidia/parakeet-tdt-0.6b-v3",
+  "contract": "parakeet-tdt-0.6b-v3-coreml",
+  "language": ["en"],
+  "minimum_os": "iOS18/macOS15",
+  "encoders": {
+    "Encoder.mlmodelc": {
+      "weight_format": "five_value_sparse_mask_lut6_8rows_fp16",
+      "size_mb": 321,
+      "recommended_compute_units": "cpuAndNeuralEngine",
+      "note": "default; exact; ANE 18.6 ms per 15 s window (159x RTFx); first ANE load compiles ~1 min; GPU load materializes the sparse weights (~150 s every launch) - use Encoder_lut3 for GPU"
+    },
+    "Encoder_sparse-g4.mlmodelc": {
+      "weight_format": "five_value_sparse_mask_lut4_4rows_fp16",
+      "size_mb": 246,
+      "recommended_compute_units": "cpuAndNeuralEngine",
+      "note": "exact; ANE 24.3 ms (140x RTFx, v3 speed); same GPU load caveat"
+    },
+    "Encoder_sparse-g1.mlmodelc": {
+      "weight_format": "five_value_sparse_mask_lut2_per_row_fp16",
+      "size_mb": 176,
+      "recommended_compute_units": "cpuAndNeuralEngine",
+      "note": "exact; smallest; ANE 70 ms (~70x RTFx); same GPU load caveat"
+    },
+    "Encoder_lut6.mlmodelc": {
+      "weight_format": "five_value_lut6_8rows_fp16",
+      "size_mb": 470,
+      "recommended_compute_units": "any",
+      "note": "exact dense palette; ANE 18.6 ms, GPU 16 ms, loads in under a second on either"
+    },
+    "Encoder_lut3.mlmodelc": {
+      "weight_format": "five_value_lut3_per_row_fp16",
+      "size_mb": 253,
+      "recommended_compute_units": "cpuAndGPU",
+      "note": "exact dense palette; GPU 16 ms with sub-second load; 72 ms on the ANE"
+    }
+  }
+}