AutoSpeech (code, models, paper)

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+AutoSpeech.[[:space:]]Neural[[:space:]]Architecture[[:space:]]Search[[:space:]]for[[:space:]]Speaker[[:space:]]Recognition.pdf filter=lfs diff=lfs merge=lfs -text
+models/ailia-models/code/wav/id10270/8jEAjG6SegY/00008.wav filter=lfs diff=lfs merge=lfs -text
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AutoSpeech. Neural Architecture Search for Speaker Recognition.pdf

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code/AutoSpeech.zip

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code/Modified-AutoSpeech.zip

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models/ailia-models/code/LICENSE

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+MIT License
+
+Copyright (c) 2020 VITA-Group
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

models/ailia-models/code/README.md

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+# AutoSpeech
+
+## Input
+
+Audio file
+```
+Wav file from The VoxCeleb1 Dataset https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html
+
+Default input: wav/id10283/oGZsanLiXsY/00004.wav
+```
+
+Please download the test data set (https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/vox1_test_wav.zip) to check various data.
+
+## Output
+
+- Identification mode  
+  Top 5 label.
+  ```
+  Top5: id10283, id11084, id10200, id11064, id10404
+  ```
+
+- Verification mode  
+  Degree of similarity.
+  ```
+  similar: 0.42575997
+  verification: match (threshold: 0.260)
+  ```
+
+## Usage
+Automatically downloads the onnx and prototxt files on the first run.
+It is necessary to be connected to the Internet while downloading.
+
+For the sample wav,
+```bash
+$ python3 auto_speech.py
+```
+It outputs top 5 label. (identification mode)
+
+If you want to specify the input file, put the path after the `--input` option.
+```bash
+$ python3 auto_speech.py --input wav/id10283/oGZsanLiXsY/00004.wav
+```
+
+When two files are specified with the `--input1` and `--input2` options,
+check if two audio files belong to the same person. (verification mode)
+```bash
+$ python3 auto_speech.py --input1 wav/id10270/8jEAjG6SegY/00008.wav --input2 wav/id10270/x6uYqmx31kE/00001.wav
+```
+
+## Reference
+
+[AutoSpeech: Neural Architecture Search for Speaker Recognition](https://github.com/VITA-Group/AutoSpeech)
+
+## Framework
+
+Pytorch
+
+## Model Format
+
+ONNX opset=11
+
+## Netron
+
+[proposed_iden.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/auto_speech/proposed_iden.onnx.prototxt)  
+[proposed_classifier.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/auto_speech/proposed_classifier.onnx.prototxt)  
+[proposed_veri.onnx.prototxt](https://netron.app/?url=https://storage.googleapis.com/ailia-models/auto_speech/proposed_veri.onnx.prototxt)

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models/ailia-models/code/auto_speech.py

@@ -0,0 +1,275 @@
+import os
+import sys
+import time
+import random
+
+import numpy as np
+import librosa
+
+import ailia
+
+# import original modules
+sys.path.append('../../util')
+from arg_utils import get_base_parser, update_parser  # noqa: E402
+from model_utils import check_and_download_models  # noqa: E402
+
+# logger
+from logging import getLogger  # noqa: E402
+
+logger = getLogger(__name__)
+
+# ======================
+# Parameters
+# ======================
+
+WEIGHT_IDENT_PATH = './proposed_iden.onnx'
+MODEL_IDENT_PATH = './proposed_iden.onnx.prototxt'
+WEIGHT_CLASSIFIER_PATH = './proposed_classifier.onnx'
+MODEL_CLASSIFIER_PATH = './proposed_classifier.onnx.prototxt'
+WEIGHT_VERI_PATH = './proposed_veri.onnx'
+MODEL_VERI_PATH = './proposed_veri.onnx.prototxt'
+REMOTE_PATH = \
+    'https://storage.googleapis.com/ailia-models/auto_speech/'
+
+WAVE_PATH = "wav/id10283/oGZsanLiXsY/00004.wav"
+
+# Audio
+SAMPLING_RATE = 16000
+
+# Mel-filterbank
+WINDOW_LENGTH = 25  # In milliseconds
+WINDOW_STEP = 10  # In milliseconds
+N_FFT = 512
+
+# Audio volume normalization
+AUDIO_NORM_TARGET_dBFS = -30
+
+THRESHOLD = 0.26
+
+INT16_MAX = (2 ** 15) - 1
+
+# ======================
+# Arguemnt Parser Config
+# ======================
+
+parser = get_base_parser(
+    'AutoSpeech', WAVE_PATH, None, input_ftype='audio'
+)
+parser.add_argument(
+    '-i1', '--input1', metavar='WAV', default=None,
+    help='Specify an wav file to compare with the input2 wav. (verification mode)'
+)
+parser.add_argument(
+    '-i2', '--input2', metavar='WAV', default=None,
+    help='Specify an wav file to compare with the input1 wav. (verification mode)'
+)
+parser.add_argument(
+    '-th', '--threshold',
+    default=THRESHOLD, type=float,
+    help='The similar threshold for verification.'
+)
+args = update_parser(parser)
+
+
+# ======================
+# Secondaty Functions
+# ======================
+
+def read_wave(path):
+    # prepare input data
+    wav, source_sr = librosa.load(path, sr=None)
+    # Resample the wav if needed
+    if source_sr is not None and source_sr != SAMPLING_RATE:
+        wav = librosa.resample(wav, source_sr, SAMPLING_RATE)
+
+    return wav
+
+
+def voxceleb1_ids():
+    with open("VoxCeleb1_ids.txt") as f:
+        ids = [x.strip() for x in f]
+
+    return ids
+
+
+def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False):
+    if increase_only and decrease_only:
+        raise ValueError("Both increase only and decrease only are set")
+    rms = np.sqrt(np.mean((wav * INT16_MAX) ** 2))
+    wave_dBFS = 20 * np.log10(rms / INT16_MAX)
+    dBFS_change = target_dBFS - wave_dBFS
+    if dBFS_change < 0 and increase_only or dBFS_change > 0 and decrease_only:
+        return wav
+    return wav * (10 ** (dBFS_change / 20))
+
+
+def wav_to_spectrogram(wav):
+    frames = np.abs(librosa.core.stft(
+        wav,
+        n_fft=N_FFT,
+        hop_length=int(SAMPLING_RATE * WINDOW_STEP / 1000),
+        win_length=int(SAMPLING_RATE * WINDOW_LENGTH / 1000),
+    ))
+    return frames.astype(np.float32).T
+
+
+def generate_sequence(feature, partial_n_frames, shift=None):
+    while feature.shape[0] <= partial_n_frames:
+        feature = np.repeat(feature, 2, axis=0)
+    if shift is None:
+        shift = partial_n_frames // 2
+    test_sequence = []
+    start = 0
+    while start + partial_n_frames <= feature.shape[0]:
+        test_sequence.append(feature[start: start + partial_n_frames])
+        start += shift
+    test_sequence = np.stack(test_sequence, axis=0)
+    return test_sequence
+
+
+def cosine_similar(a, b, data_is_normalized=False):
+    if not data_is_normalized:
+        a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)
+        b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)
+    return np.dot(a, b.T)
+
+
+# ======================
+# Main functions
+# ======================
+
+def preprocess(wav):
+    wav = normalize_volume(wav, AUDIO_NORM_TARGET_dBFS, increase_only=True)
+    feature = wav_to_spectrogram(wav)
+
+    sequence = generate_sequence(feature, partial_n_frames=300)
+
+    mean = np.load('mean.npy')
+    std = np.load('std.npy')
+    sequence = (sequence - mean) / std
+    # if random.random() < 0.5:
+    #     sequence = np.flip(sequence, axis=0).copy()
+
+    return sequence
+
+
+def predict(wav, net, net_classifier=None):
+    # initial preprocesses
+    sequence = preprocess(wav)
+
+    # feedforward
+    output = net.predict([sequence])
+    output = output[0]
+
+    output = np.mean(output, axis=0, keepdims=True)
+
+    if not net_classifier:
+        return output
+
+    output = net_classifier.predict([output])
+    output = output[0]
+
+    idx = np.argsort(output[0])[::-1]
+
+    return idx
+
+
+def eval_identification(net, net_classifier):
+    ids = voxceleb1_ids()
+
+    for input_path in args.input:
+        logger.info(f'input: {input_path}')
+
+        # prepare input data
+        wav = read_wave(input_path)
+
+        # inference
+        logger.info('Start inference...')
+        if args.benchmark:
+            logger.info('BENCHMARK mode')
+            for i in range(5):
+                start = int(round(time.time() * 1000))
+                idx = predict(wav, net, net_classifier)
+                end = int(round(time.time() * 1000))
+                logger.info(f'\tailia processing time {end - start} ms')
+        else:
+            idx = predict(wav, net, net_classifier)
+
+        logger.info(' Top5: %s' % ', '.join([ids[i] for i in idx[:5]]))
+
+    logger.info('Script finished successfully.')
+
+
+def eval_verification(net):
+    threshold = args.threshold
+    input1 = args.input1
+    input2 = args.input2
+
+    if input1 is None:
+        logger.error('input1 is not specified')
+        sys.exit(-1)
+    elif not os.path.isfile(input1):
+        logger.error('specified input1 is not file path nor directory path')
+        sys.exit(-1)
+    if input2 is None:
+        logger.error('input2 is not specified')
+        sys.exit(-1)
+    elif not os.path.isfile(input2):
+        logger.error('specified input2 is not file path nor directory path')
+        sys.exit(-1)
+
+    logger.info(f'input1: {input1}')
+    logger.info(f'input2: {input2}')
+
+    # prepare input data
+    wav1 = read_wave(args.input1)
+    wav2 = read_wave(args.input2)
+
+    # inference
+    logger.info('Start inference...')
+    if args.benchmark:
+        logger.info('BENCHMARK mode')
+        for i in range(5):
+            start = int(round(time.time() * 1000))
+            output = predict(wav1, net)
+            output2 = predict(wav2, net)
+            end = int(round(time.time() * 1000))
+            logger.info(f'\tailia processing time {end - start} ms')
+    else:
+        output = predict(wav1, net)
+        output2 = predict(wav2, net)
+
+    similar = cosine_similar(output, output2)
+    logger.info(' similar: %.8f' % similar[0])
+    logger.info(' verification: %s (threshold: %.3f)' %
+                ('match' if similar[0] >= threshold else 'unmatch', threshold))
+
+    logger.info('Script finished successfully.')
+
+
+def main():
+    # model files check and download
+    if args.input1 or args.input2:
+        check_and_download_models(WEIGHT_VERI_PATH, MODEL_VERI_PATH, REMOTE_PATH)
+    else:
+        logger.info('Checking identification model...')
+        check_and_download_models(WEIGHT_IDENT_PATH, MODEL_IDENT_PATH, REMOTE_PATH)
+        logger.info('Checking classification model...')
+        check_and_download_models(WEIGHT_CLASSIFIER_PATH, MODEL_CLASSIFIER_PATH, REMOTE_PATH)
+
+    env_id = args.env_id
+
+    if args.input1 or args.input2:
+        net = ailia.Net(MODEL_VERI_PATH, WEIGHT_VERI_PATH, env_id=env_id)
+
+        eval_verification(net)
+    else:
+        # initialize
+        net = ailia.Net(MODEL_IDENT_PATH, WEIGHT_IDENT_PATH, env_id=env_id)
+        net_classifier = ailia.Net(MODEL_CLASSIFIER_PATH, WEIGHT_CLASSIFIER_PATH, env_id=env_id)
+
+        eval_identification(net, net_classifier)
+
+
+if __name__ == '__main__':
+    main()

models/ailia-models/code/mean.npy

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+oid sha256:bb3a4a40ee2f3a8b306d6abf0928de4137264b22ceff4ca70f761de3a1ddba18
+size 1156

models/ailia-models/code/std.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9fe00c0f076c78b087e1ef31b13aa38405f6643b3888a8ceb166dc58b4b51dd0
+size 1156

models/ailia-models/code/wav/id10270/8jEAjG6SegY/00008.wav

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+size 217646

models/ailia-models/code/wav/id10270/x6uYqmx31kE/00001.wav

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+size 181806

models/ailia-models/code/wav/id10282/zGjY8J48FoE/00004.wav

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+size 154926

models/ailia-models/code/wav/id10283/oGZsanLiXsY/00004.wav

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+size 193326

models/ailia-models/code/wav/id10291/oLJhH76d0kU/00004.wav

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models/ailia-models/proposed_classifier.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:334c81f1d939a361affbdf933d5d9c035971cb7f07962c2d6a1fc8444b8cff59
+size 10253474

models/ailia-models/proposed_classifier.onnx.prototxt

@@ -0,0 +1,76 @@
+ir_version: 6
+producer_name: "pytorch"
+producer_version: "1.9"
+model_version: 0
+graph {
+  name: "torch-jit-export"
+  node {
+    input: "input"
+    input: "classifier.weight"
+    input: "classifier.bias"
+    output: "output"
+    name: "Gemm_0"
+    op_type: "Gemm"
+    attribute {
+      name: "alpha"
+      f: 1.0
+      type: FLOAT
+    }
+    attribute {
+      name: "beta"
+      f: 1.0
+      type: FLOAT
+    }
+    attribute {
+      name: "transB"
+      i: 1
+      type: INT
+    }
+  }
+  initializer {
+      dims: 1251
+      dims: 2048
+      data_type: 1
+      name: "classifier.weight"
+  }
+  initializer {
+      dims: 1251
+      data_type: 1
+      name: "classifier.bias"
+  }
+  input {
+    name: "input"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_param: "n"
+          }
+          dim {
+            dim_value: 2048
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "output"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_param: "n"
+          }
+          dim {
+            dim_value: 1251
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 11
+}

models/ailia-models/proposed_iden.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c3ca35732d8784a15c8fb6bd435e3c038eb4f62ed8a634b1a70e24df1b940ca9
+size 60384076

models/ailia-models/proposed_veri.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:88a24fe7ddee235626f57ab53a89cf8f6b86d4a563b61bf87072c500b3855c74
+size 60384096

models/ailia-models/source.txt

@@ -0,0 +1,10 @@
+https://github.com/axinc-ai/ailia-models/tree/master/audio_processing/auto_speech
+
+https://storage.googleapis.com/ailia-models/auto_speech/proposed_iden.onnx
+https://storage.googleapis.com/ailia-models/auto_speech/proposed_iden.onnx.prototxt
+
+https://storage.googleapis.com/ailia-models/auto_speech/proposed_classifier.onnx
+https://storage.googleapis.com/ailia-models/auto_speech/proposed_classifier.onnx.prototxt
+
+https://storage.googleapis.com/ailia-models/auto_speech/proposed_veri.onnx
+https://storage.googleapis.com/ailia-models/auto_speech/proposed_veri.onnx.prototxt