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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
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"/home/foxconnhy/miniconda3/envs/llamafactory/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"/home/jeff/.cache/huggingface/modules/transformers_modules/gemma_v1/speech_conformer_encoder.py:2798: FutureWarning: Please specify CheckpointImpl.NO_REENTRANT as CheckpointImpl.REENTRANT will soon be removed as the default and eventually deprecated.\n",
" lambda i: encoder_checkpoint_wrapper(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"######################## speech lora #############\n",
"######################## text lora #############\n"
]
},
{
"name": "stderr",
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"text": [
"Loading checkpoint shards: 100%|██████████| 3/3 [00:01<00:00, 1.80it/s]\n",
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'language_model.model.base_model.model.layers.9.self_attn.o_proj.lora_B.text.weight', 'language_model.model.base_model.model.layers.9.self_attn.q_proj.lora_A.text.weight', 'language_model.model.base_model.model.layers.9.self_attn.q_proj.lora_B.text.weight', 'language_model.model.base_model.model.layers.9.self_attn.v_proj.lora_A.text.weight', 'language_model.model.base_model.model.layers.9.self_attn.v_proj.lora_B.text.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
"Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n"
]
}
],
"source": [
"from io import BytesIO\n",
"import torch\n",
"import numpy as np\n",
"from transformers import AutoModel, AutoProcessor, BatchFeature,Gemma3ForCausalLM,Gemma3Processor\n",
"\n",
"# converter = opencc.OpenCC('s2tw.json')\n",
"\n",
"model_id = \"/mnt/data-2t/jeff/codes/llm/cpp/gemma_v1\"\n",
"revision = \"main\" #\"v1.0\"\n",
"\n",
"model = AutoModel.from_pretrained(\n",
" model_id, device_map=\"cpu\", revision = revision, trust_remote_code=True\n",
").eval()\n",
"\n",
"processor = AutoProcessor.from_pretrained(\n",
" model_id, revision = revision, trust_remote_code=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Sequential(\n",
" (0): Linear(in_features=1024, out_features=2560, bias=True)\n",
" (1): GELU(approximate='none')\n",
" (2): Linear(in_features=2560, out_features=2560, bias=True)\n",
")"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.audio_tower\n",
"model.audio_projector"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [],
"source": [
"from ASRDataset import *\n",
"pickup_dataset = MultiturnAudioDataset(split='train',processor=processor,json_path='/mnt/data-2t/jeff/codes/llm/cpp/sample_data/pickup_processed.json')"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 256, 80])\n",
"torch.Size([1, 217, 80])\n",
"torch.Size([1, 77, 80])\n",
"torch.Size([1, 580, 80])\n"
]
}
],
"source": [
"for i in range(len(pickup_dataset)):\n",
" inp = pickup_dataset.__getitem__(i)\n",
" print(inp['input_audio_embeds'].shape)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 100, 2560])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inp = pickup_dataset.__getitem__(3)\n",
"fea,mask = model.audio_tower(inp['input_audio_embeds'],torch.ones(inp['input_audio_embeds'].shape[:2]))\n",
"model.audio_projector(fea).shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch \n",
"import torch.nn as nn\n",
"from speech_conformer_encoder import ConformerEncoder\n",
"class Gemma3AudioEncoder(nn.Module):\n",
" def __init__(self,):\n",
" super().__init__()\n",
" audio_config = model.config.audio_config.to_diff_dict()\n",
" for item in ['transformers_version', 'model_type', 'torch_dtype']:\n",
" if item in audio_config:\n",
" audio_config.pop(item)\n",
" # self.audio_tower = model.audio_tower\n",
" # self.audio_projector = model.audio_projector\n",
" self.audio_tower = ConformerEncoder(**audio_config)#model.audio_tower\n",
" self.audio_projector = nn.Sequential(\n",
" nn.Linear(in_features=1024, out_features=2560, bias=True),\n",
" nn.GELU(approximate='none'),\n",
" nn.Linear(in_features=2560, out_features=2560, bias=True))#model.audio_projector\n",
" def forward(self,x,mask):\n",
" # mask = torch.ones(x.shape[:2])\n",
" x,_ = self.audio_tower(x,mask)\n",
" x = self.audio_projector(x)\n",
" return x\n",
"audio_encoder = Gemma3AudioEncoder()\n",
"import copy\n",
"audio_encoder.audio_tower.encoder_embedding=copy.deepcopy(model.audio_tower.encoder_embedding)\n",
"audio_encoder.audio_projector.load_state_dict(model.audio_projector.state_dict())\n",
"audio_encoder.audio_tower.load_state_dict(model.audio_tower.state_dict())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import onnx\n",
"import onnxruntime as ort\n",
"import onnxscript\n",
"import os\n",
"import requests\n",
"import shutil\n",
"import soundfile\n",
"import subprocess\n",
"import sys\n",
"import torch\n",
"\n",
"from onnx import helper, numpy_helper, TensorProto\n",
"from onnxruntime_genai.models.builder import create_model\n",
"from onnxruntime.transformers.dynamo_onnx_helper import DynamoOnnxHelper\n",
"from onnxscript import ir\n",
"from torch.export import Dim, export\n",
"def build_speech(outputdir='./onnx_files'):\n",
" # TorchScript export\n",
" dummy_inputs = (\n",
" torch.randn((1,97,80)),\n",
" torch.ones((1,97))\n",
" #inputs[\"input_audio_embeds\"], # audio_embeds: torch.FloatTensor\n",
" #inputs[\"audio_attention_mask\"], # audio_attention_mask: torch.BoolTensor\n",
" # inputs[\"audio_embed_sizes\"], # audio_sizes: torch.LongTensor\n",
" # inputs[\"input_mode\"], # audio_projection_mode: int\n",
" )\n",
" filename = \"phi-4-mm-speech.onnx\"\n",
"\n",
" temp_folder_1 = os.path.join(outputdir, \"speech_init_export\")\n",
" os.makedirs(temp_folder_1, exist_ok=True)\n",
"\n",
" fpath_1 = os.path.join(temp_folder_1, filename)\n",
" torch._dynamo.config.capture_scalar_outputs = True\n",
" onnx_program = torch.onnx.export(audio_encoder, dummy_inputs, fpath_1,\n",
" input_names=[\"audio_embeds\", \"audio_attention_mask\"], \n",
" output_names=[\"audio_features\"],\n",
" opset_version=20,\n",
" dynamic_axes={\n",
" \"audio_embeds\": {0:'B',1: \"L\"},\n",
" \"audio_attention_mask\": {0:'B',1: \"L\"},\n",
" },\n",
" )\n",
"\n",
"build_speech()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"import onnxruntime as ort\n",
"import numpy as np\n",
"ort_sess = ort.InferenceSession(\"/mnt/data-2t/jeff/codes/llm/cpp/onnx_files/speech_init_export/phi-4-mm-speech.onnx\")"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2, 111, 2560)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"from tqdm import tqdm\n",
"import torch\n",
"import numpy as np\n",
"a=[]\n",
"# for i in tqdm(range(10000)):\n",
"# try:\n",
"ort_sess.run(None, {\"audio_embeds\": np.array(torch.randn(1,97,80),dtype=np.float32),\n",
" # \"audio_attention_mask\":np.ones((1,97),dtype=np.float32)\n",
" }\n",
" )\n",
" # print(i)\n",
" # a.append(i)\n",
" # except:\n",
" # pass\n",
"ort_sess.run(None, {\"audio_embeds\": np.array(torch.randn(2,888,80),dtype=np.float32),\n",
" # \"audio_attention_mask\":np.ones((2,97),dtype=np.float32)\n",
" }\n",
" )[0].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python inference time check"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"total = 0\n",
"_mel = speechlib_mel(16000, 512, 80, fmin=None, fmax=16000//2-80-230).T\n",
"for i in range(100):\n",
" now = time.time()\n",
" inp = np.random.randn(np.random.randint(16240, 48240)).reshape(1,-1)#np.array(torch.randn(1,np.random.randint(100,300),80),dtype=np.float32)\n",
" inp = _extract_features(inp,16000).reshape(1,-1,80)\n",
" now = time.time()\n",
" # inp = np.array(torch.randn(1,150,80),dtype=np.float32)\n",
" ort_sess.run(None, {\"audio_embeds\": inp,\n",
" # \"audio_attention_mask\":np.ones((1,97),dtype=np.float32)\n",
" })\n",
" total += time.time()-now\n",
" \n",
"total,total/100"
]
},
{
"cell_type": "code",
"execution_count": 238,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"24240"
]
},
"execution_count": 238,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"149*160+400"
]
},
{
"cell_type": "code",
"execution_count": 233,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 40917)"
]
},
"execution_count": 233,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.randn(np.random.randint(16240, 48240)).reshape(1,-1).shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10.608245611190796, 0.10608245611190796)"
]
},
"execution_count": 218,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import time\n",
"total = 0\n",
"for i in range(100):\n",
" tmp = torch.randn(1,np.random.randint(100,300),80)\n",
" mask = torch.ones(tmp.shape[:2])\n",
" now = time.time()\n",
" audio_encoder(tmp,mask)\n",
" total += time.time()-now\n",
"total,total/100"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# C++ ERROR check"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[[ 0.3246, 0.0295, 0.1076, ..., -0.1125, -0.0894, -0.3800],\n",
" [ 0.3267, -0.2442, 0.2653, ..., 0.7783, -0.6049, -1.0858],\n",
" [ 0.1797, 0.0438, 0.9673, ..., 0.5126, -0.5657, -0.7050],\n",
" ...,\n",
" [ 0.0261, -0.0324, 0.0230, ..., -0.1303, 0.0343, 0.1486],\n",
" [ 0.1655, -0.3327, 0.4232, ..., 0.0513, 0.4222, -0.3645],\n",
" [ 0.1147, -0.1201, 0.4198, ..., 0.6170, 0.0838, -0.1409]]],\n",
" grad_fn=<ViewBackward0>)"
]
},
"execution_count": 167,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inp = pickup_dataset.__getitem__(3)\n",
"fea,mask = model.audio_tower(inp['input_audio_embeds'],torch.ones(inp['input_audio_embeds'].shape[:2]))\n",
"model.audio_projector(fea)"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ 0.13004433, -0.06643961, 0.01333247, ..., -0.05643693,\n",
" -0.23922557, 0.569423 ],\n",
" [-0.75552 , -0.05047493, -0.82725084, ..., 0.32261163,\n",
" -0.14968234, -0.7078437 ],\n",
" [-0.6673857 , 0.33906737, -0.6191502 , ..., 0.04259709,\n",
" -0.01194861, 0.27635992],\n",
" ...,\n",
" [ 0.02916821, -0.03163592, 0.02736526, ..., -0.12979224,\n",
" 0.03317374, 0.15346158],\n",
" [-0.8559882 , -0.5196625 , 0.2549707 , ..., 0.28192428,\n",
" 1.4099622 , -0.15940394],\n",
" [-0.20253824, -0.30478072, -0.6786582 , ..., 0.08860758,\n",
" -0.12145798, 0.525889 ]]], dtype=float32)"
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inp = pickup_dataset.__getitem__(0)\n",
"res = ort_sess.run(None, {\"audio_embeds\": np.array(inp['input_audio_embeds'],dtype=np.float32),\n",
" # \"audio_attention_mask\":np.ones((2,97),dtype=np.float32)\n",
" }\n",
" )[0]\n",
"res"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ 0.130969 , -0.0697925, 0.0150866, ..., -0.0559536,\n",
" -0.239062 , 0.567436 ],\n",
" [-0.753288 , -0.0582227, -0.825365 , ..., 0.320587 ,\n",
" -0.153626 , -0.709664 ],\n",
" [-0.656874 , 0.342632 , -0.607641 , ..., 0.0383743,\n",
" -0.0218912, 0.269968 ],\n",
" ...,\n",
" [ 0.0291714, -0.0316175, 0.027369 , ..., -0.129825 ,\n",
" 0.033166 , 0.153453 ],\n",
" [-0.854555 , -0.530883 , 0.258313 , ..., 0.279057 ,\n",
" 1.40658 , -0.159066 ],\n",
" [-0.197598 , -0.306157 , -0.67907 , ..., 0.0915015,\n",
" -0.124402 , 0.52159 ]]])"
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f = open('/mnt/data-2t/jeff/codes/llm/cpp/inference/f0.txt')\n",
"content = f.readlines()\n",
"f.close()\n",
"audio_fea_cpp = np.array([float(i) for i in content[0].split(',')]).reshape(1,-1,2560)\n",
"audio_fea_cpp"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([[[0.917797, 1.33496 , 1.9894 , ..., 6.60723 , 6.95787 ,\n",
" 7.20139 ],\n",
" [0. , 0. , 0. , ..., 5.99914 , 6.11214 ,\n",
" 6.40908 ],\n",
" [0. , 0. , 0. , ..., 5.1184 , 5.36291 ,\n",
" 5.14623 ],\n",
" ...,\n",
" [0. , 0. , 0. , ..., 6.25256 , 6.29312 ,\n",
" 7.05511 ],\n",
" [0. , 0. , 0. , ..., 6.49829 , 6.7198 ,\n",
" 7.08144 ],\n",
" [0. , 0. , 1.08376 , ..., 5.43068 , 5.97577 ,\n",
" 6.35748 ]]]),\n",
" tensor([[[0.8826, 1.3054, 1.9652, ..., 6.6069, 6.9578, 7.2011],\n",
" [0.0000, 0.0000, 0.0000, ..., 5.9991, 6.1121, 6.4091],\n",
" [0.0000, 0.0000, 0.0000, ..., 5.1147, 5.3624, 5.1428],\n",
" ...,\n",
" [0.0000, 0.0000, 0.0000, ..., 6.2526, 6.2931, 7.0548],\n",
" [0.0000, 0.0000, 0.0000, ..., 6.4981, 6.7198, 7.0807],\n",
" [0.0000, 0.0000, 1.1479, ..., 5.4311, 5.9743, 6.3568]]]))"
]
},
"execution_count": 202,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f = open('/mnt/data-2t/jeff/codes/llm/cpp/inference/matrix_output.txt')\n",
"txtlines = f.readlines()\n",
"f.close()\n",
"inp_emb_cpp = np.array([float(i) for l in txtlines for i in l.split(',')]).reshape(1,-1,80)\n",
"inp_emb_cpp,pickup_dataset.__getitem__(0)['input_audio_embeds']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python preprocessor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(353, 80)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# modify the code : \n",
"# 1. input model and input pcm from args. \n",
"# 2. add model input preprocessor by following python code. The wav input of _extract_features which is an audio array\n",
"# 3. the onnx model input is [batch,frames,feature size] = [-1,-1,80]\n",
"\n",
"def _extract_spectrogram(wav, fs):\n",
" \"\"\"Extract spectrogram features from waveform.\n",
" Args:\n",
" wav (1D array): waveform of the input\n",
" fs (int): sampling rate of the waveform, 16000.\n",
" Output:\n",
" log_fbank (2D array): a TxD matrix of log Mel filterbank features.\n",
" D=80, and T is the number of frames.\n",
" \"\"\"\n",
" if wav.ndim > 1:\n",
" wav = np.squeeze(wav)\n",
"\n",
" # by default, we extract the mean if stereo\n",
" if len(wav.shape) == 2:\n",
" wav = wav.mean(1)\n",
"\n",
" preemphasis = 0.97\n",
" n_fft = 512\n",
" win_length = 400\n",
" hop_length = 160\n",
" fft_window = np.hamming(400)\n",
"\n",
" # Spec 1: SpeechLib cut remaining sample insufficient for a hop\n",
" n_batch = (wav.shape[0] - win_length) // hop_length + 1\n",
" # Here we don't use stride_tricks since the input array may not satisfy\n",
" # memory layout requirement and we need writeable output\n",
" # Here we only use list of views before copy to desination\n",
" # so it is more efficient than broadcasting\n",
" y_frames = np.array(\n",
" [wav[_stride : _stride + win_length] for _stride in range(0, hop_length * n_batch, hop_length)],\n",
" dtype=np.float32,\n",
" )\n",
"\n",
" # Spec 2: SpeechLib applies preemphasis within each batch\n",
" y_frames_prev = np.roll(y_frames, 1, axis=1)\n",
" y_frames_prev[:, 0] = y_frames_prev[:, 1]\n",
" y_frames = (y_frames - preemphasis * y_frames_prev) * 32768\n",
"\n",
" S = np.fft.rfft(fft_window * y_frames, n=n_fft, axis=1).astype(np.complex64)\n",
" spec = np.abs(S).astype(np.float32)\n",
" return spec\n",
"def speechlib_mel(sample_rate, n_fft, n_mels, fmin=None, fmax=None):\n",
" \"\"\"Create a Mel filter-bank the same as SpeechLib FbankFC.\n",
"\n",
" Args:\n",
" sample_rate (int): Sample rate in Hz. number > 0 [scalar]\n",
" n_fft (int): FFT size. int > 0 [scalar]\n",
" n_mel (int): Mel filter size. int > 0 [scalar]\n",
" fmin (float): lowest frequency (in Hz). If None use 0.0.\n",
" float >= 0 [scalar]\n",
" fmax: highest frequency (in Hz). If None use sample_rate / 2.\n",
" float >= 0 [scalar]\n",
"\n",
" Returns\n",
" out (numpy.ndarray): Mel transform matrix\n",
" [shape=(n_mels, 1 + n_fft/2)]\n",
" \"\"\"\n",
"\n",
" bank_width = int(n_fft // 2 + 1)\n",
" if fmax is None:\n",
" fmax = sample_rate / 2\n",
" if fmin is None:\n",
" fmin = 0\n",
" assert fmin >= 0, \"fmin cannot be negtive\"\n",
" assert fmin < fmax <= sample_rate / 2, \"fmax must be between (fmin, samplerate / 2]\"\n",
"\n",
" def mel(f):\n",
" return 1127.0 * np.log(1.0 + f / 700.0)\n",
"\n",
" def bin2mel(fft_bin):\n",
" return 1127.0 * np.log(1.0 + fft_bin * sample_rate / (n_fft * 700.0))\n",
"\n",
" def f2bin(f):\n",
" return int((f * n_fft / sample_rate) + 0.5)\n",
"\n",
" # Spec 1: FFT bin range [f2bin(fmin) + 1, f2bin(fmax) - 1]\n",
" klo = f2bin(fmin) + 1\n",
" khi = f2bin(fmax)\n",
"\n",
" khi = max(khi, klo)\n",
"\n",
" # Spec 2: SpeechLib uses trianges in Mel space\n",
" mlo = mel(fmin)\n",
" mhi = mel(fmax)\n",
" m_centers = np.linspace(mlo, mhi, n_mels + 2)\n",
" ms = (mhi - mlo) / (n_mels + 1)\n",
"\n",
" matrix = np.zeros((n_mels, bank_width), dtype=np.float32)\n",
" for m in range(0, n_mels):\n",
" left = m_centers[m]\n",
" center = m_centers[m + 1]\n",
" right = m_centers[m + 2]\n",
" for fft_bin in range(klo, khi):\n",
" mbin = bin2mel(fft_bin)\n",
" if left < mbin < right:\n",
" matrix[m, fft_bin] = 1.0 - abs(center - mbin) / ms\n",
"\n",
" return matrix\n",
"\n",
"def _extract_features(wav, fs):\n",
" \"\"\"Extract log filterbank features from waveform.\n",
" Args:\n",
" wav (1D array): waveform of the input\n",
" fs (int): sampling rate of the waveform, 16000 or 8000.\n",
" If fs=8000, the waveform will be resampled to 16000Hz.\n",
" Output:\n",
" log_fbank (2D array): a TxD matrix of log Mel filterbank features.\n",
" D=80, and T is the number of frames.\n",
" \"\"\"\n",
" spec = _extract_spectrogram(wav, fs)\n",
" spec_power = spec**2\n",
"\n",
" fbank_power = np.clip(spec_power.dot(_mel), 1.0, None)\n",
" log_fbank = np.log(fbank_power).astype(np.float32)\n",
"\n",
" return log_fbank\n",
"\n",
"## example \n",
"## input shape of arr is [1, 56832], output shape will be (353,80)\n",
"_mel = speechlib_mel(16000, 512, 80, fmin=None, fmax=16000//2-80-230).T\n",
"output = _extract_features(arr,16000)"
]
},
{
"cell_type": "code",
"execution_count": 227,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(256, 80)"
]
},
"execution_count": 227,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_mel = speechlib_mel(16000, 512, 80, fmin=None, fmax=16000//2-80-230).T\n",
"output = _extract_features(arr,16000)\n",
"output.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"256"
]
},
"execution_count": 228,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(41239-400)//160+1 100~300"
]
},
{
"cell_type": "code",
"execution_count": 229,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(16240, 48240)"
]
},
"execution_count": 229,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"99*160+400,299*160+400"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "llamafactory",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.16"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|