add test_load.py

paddlespeech/audio/_extension.py

@@ -103,7 +103,6 @@ def _load_lib(lib: str) -> bool:
             If a dependency is missing, then users have to install it.
     """
     path = _get_lib_path(lib)
-    warnings.warn("lib path is :" + str(path))
     if not path.exists():
         warnings.warn("lib path is not exists:" + str(path))
         return False

paddlespeech/audio/backends/sox_io_backend.py

@@ -8,7 +8,7 @@ from paddle import Tensor
 from .common import AudioMetaData
 
 from paddlespeech.audio._internal import module_utils  as _mod_utils
-from paddlespeech.aduio import _paddleaudio as paddleaudio 
+from paddlespeech.audio import _paddleaudio as paddleaudio 
 
 #https://github.com/pytorch/audio/blob/main/torchaudio/backend/sox_io_backend.py
 

paddlespeech/audio/src/pybind/sox/utils.cpp

@@ -178,36 +178,68 @@ py::array convert_to_tensor(
     const py::dtype dtype,
     const bool normalize,
     const bool channels_first) {
+  // todo refector later(SGoat)
   py::array t;
   uint64_t dummy = 0;
   SOX_SAMPLE_LOCALS;
+  int32_t num_rows = num_samples / num_channels;
   if (normalize || dtype.char_() == 'f') {
-    t = py::array(dtype, {num_samples / num_channels, num_channels});
+    t = py::array(dtype, {num_rows, num_channels});
     auto ptr = (float*)t.mutable_data(0, 0);
     for (int32_t i = 0; i < num_samples; ++i) {
       ptr[i] = SOX_SAMPLE_TO_FLOAT_32BIT(buffer[i], dummy);
     }
+    if (channels_first) {
+    py::array t2 = py::array(dtype, {num_channels, num_rows});
+    for (int32_t row_idx = 0; row_idx < num_channels; ++row_idx) {
+      for (int32_t col_idx = 0; col_idx < num_rows; ++col_idx)
+       *(float*)t2.mutable_data(row_idx, col_idx) = *(float*)t.data(col_idx, row_idx);
+    }
+    return t2;
+  }
   } else if (dtype.char_() == 'i') {
-    //t = torch::from_blob(
+    t = py::array(dtype, {num_rows, num_channels});
-    //        buffer, {num_samples / num_channels, num_channels}, torch::kInt32)
-    //        .clone();
-    t = py::array(dtype, {num_samples / num_channels, num_channels});
     auto ptr = (int*)t.mutable_data(0, 0);
     for (int32_t i = 0; i < num_samples; ++i) {
       ptr[i] = buffer[i];
     }
+    if (channels_first) {
+      py::array t2 = py::array(dtype, {num_channels, num_rows});
+      for (int32_t row_idx = 0; row_idx < num_channels; ++row_idx) {
+        for (int32_t col_idx = 0; col_idx < num_rows; ++col_idx)
+          *(int*)t2.mutable_data(row_idx, col_idx) = *(int*)t.data(col_idx, row_idx);
+      }
+      return t2;
+    }
   } else if (dtype.char_() == 'h') { // int16
-    t = py::array(dtype, {num_samples / num_channels, num_channels});
+    t = py::array(dtype, {num_rows, num_channels});
     auto ptr = (int16_t*)t.mutable_data(0, 0);
     for (int32_t i = 0; i < num_samples; ++i) {
       ptr[i] = SOX_SAMPLE_TO_SIGNED_16BIT(buffer[i], dummy);
     }
+    if (channels_first) {
+      py::array t2 = py::array(dtype, {num_channels, num_rows});
+      for (int32_t row_idx = 0; row_idx < num_channels; ++row_idx) {
+        for (int32_t col_idx = 0; col_idx < num_rows; ++col_idx)
+          *(int16_t*)t2.mutable_data(row_idx, col_idx) = *(int16_t*)t.data(col_idx, row_idx);
+      }
+      return t2;
+    }
   } else if (dtype.char_() == 'b') {
     //t = torch::empty({num_samples / num_channels, num_channels}, torch::kUInt8);
+    t = py::array(dtype, {num_rows, num_channels});
     auto ptr = (uint8_t*)t.mutable_data(0,0);
     for (int32_t i = 0; i < num_samples; ++i) {
       ptr[i] = SOX_SAMPLE_TO_UNSIGNED_8BIT(buffer[i], dummy);
     }
+    if (channels_first) {
+      py::array t2 = py::array(dtype, {num_channels, num_rows});
+      for (int32_t row_idx = 0; row_idx < num_channels; ++row_idx) {
+        for (int32_t col_idx = 0; col_idx < num_rows; ++col_idx)
+        *(uint8_t*)t2.mutable_data(row_idx, col_idx) = *(uint8_t*)t.data(col_idx, row_idx);
+      }
+      return t2;
+    }
   } else {
     throw std::runtime_error("Unsupported dtype.");
   }

tests/unit/audio/backends/sox_io/common.py

@@ -0,0 +1,32 @@
+
+def get_encoding(ext, dtype):
+    exts = {
+        "mp3",
+        "flac",
+        "vorbis",
+    }
+    encodings = {
+        "float32": "PCM_F",
+        "int32": "PCM_S",
+        "int16": "PCM_S",
+        "uint8": "PCM_U",
+    }
+    return ext.upper() if ext in exts else encodings[dtype]
+
+
+def get_bit_depth(dtype):
+    bit_depths = {
+        "float32": 32,
+        "int32": 32,
+        "int16": 16,
+        "uint8": 8,
+    }
+    return bit_depths[dtype]
+
+def get_bits_per_sample(ext, dtype):
+    bits_per_samples = {
+        "flac": 24,
+        "mp3": 0,
+        "vorbis": 0,
+    }
+    return bits_per_samples.get(ext, get_bit_depth(dtype))

tests/unit/audio/backends/sox_io/info_test.py

@@ -0,0 +1,34 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import unittest
+
+import numpy as np
+import paddle
+
+from paddlespeech.audio.backends import sox_io_backend
+
+class TestInfo(unittest.TestCase):
+
+    def test_wav(self, dtype, sample_rate, num_channels, sample_size):
+        """check wav file correctly """
+        path = 'testdata/test.wav'
+        info = sox_io_backend.get_info_file(path)
+        assert info.sample_rate == sample_rate
+        assert info.num_frames == sample_size # duration*sample_rate
+        assert info.num_channels == num_channels
+        assert info.bits_per_sample == get_bit_depth(dtype)
+        assert info.encoding == get_encoding('wav', dtype)
+        
+if __name__ == '__main__':
+    unittest.main()

tests/unit/audio/backends/sox_io/load_test.py

@@ -0,0 +1,47 @@
+import unittest
+import itertools
+
+from parameterized import parameterized
+import numpy as np
+from paddlespeech.audio._internal import module_utils as _mod_utils
+from paddlespeech.audio.backends import sox_io_backend
+
+from tests.unit.common_utils import (
+    get_wav_data,
+    load_wav,
+    save_wav,
+)
+
+#code is from:https://github.com/pytorch/audio/blob/main/torchaudio/test/torchaudio_unittest/backend/sox_io/load_test.py
+
+class TestLoad(unittest.TestCase):
+
+    def assert_wav(self, dtype, sample_rate, num_channels, normalize, duration):
+        """`sox_io_backend.load` can load wav format correctly.
+
+        Wav data loaded with sox_io backend should match those with scipy
+        """
+        path = 'testdata/reference.wav'
+        data = get_wav_data(dtype, num_channels, normalize=normalize, num_frames=duration * sample_rate)
+        save_wav(path, data, sample_rate)
+        expected = load_wav(path, normalize=normalize)[0]
+        data, sr = sox_io_backend.load(path, normalize=normalize)
+        assert sr == sample_rate
+        np.testing.assert_array_almost_equal(data, expected, decimal=4)
+
+    @parameterized.expand(
+        list(
+            itertools.product(
+                 ["float64", "float32", "int32",],
+                [8000, 16000],
+                [1, 2],
+                [False, True],
+            )
+        ),
+    )
+    def test_wav(self, dtype, sample_rate, num_channels, normalize):
+        """`sox_io_backend.load` can load wav format correctly."""
+        self.assert_wav(dtype, sample_rate, num_channels, normalize, duration=1)
+    
+if __name__ == '__main__':
+    unittest.main()

tests/unit/audio/backends/sox_io/save_test.py

@@ -0,0 +1,34 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import unittest
+
+import numpy as np
+import paddle
+
+from paddlespeech.audio.backends import sox_io_backend
+
+class TestInfo(unittest.TestCase):
+
+    def test_wav(self, dtype, sample_rate, num_channels, sample_size):
+        """check wav file correctly """
+        path = 'testdata/test.wav'
+        info = sox_io_backend.get_info_file(path)
+        assert info.sample_rate == sample_rate
+        assert info.num_frames == sample_size # duration*sample_rate
+        assert info.num_channels == num_channels
+        assert info.bits_per_sample == get_bit_depth(dtype)
+        assert info.encoding == get_encoding('wav', dtype)
+        
+if __name__ == '__main__':
+    unittest.main()

tests/unit/audio/backends/sox_io/testdata

@@ -0,0 +1 @@
+../../features/testdata

tests/unit/common_utils/__init__.py

@@ -0,0 +1,8 @@
+from .wav_utils import get_wav_data, load_wav, save_wav, normalize_wav
+
+__all__ = [
+    "get_wav_data",
+    "load_wav",
+    "save_wav",
+    "normalize_wav"
+]

tests/unit/common_utils/wav_utils.py

@@ -0,0 +1,92 @@
+from typing import Optional
+
+import scipy.io.wavfile
+import paddle
+
+def normalize_wav(tensor: paddle.Tensor) -> paddle.Tensor:
+    if tensor.dtype == paddle.float32:
+        pass
+    elif tensor.dtype == paddle.int32:
+        tensor = paddle.cast(tensor, paddle.float32)
+        tensor[tensor > 0] /= 2147483647.0
+        tensor[tensor < 0] /= 2147483648.0
+    elif tensor.dtype == paddle.int16:
+        tensor = paddle.cast(tensor, paddle.float32)
+        tensor[tensor > 0] /= 32767.0
+        tensor[tensor < 0] /= 32768.0
+    elif tensor.dtype == paddle.uint8:
+        tensor = paddle.cast(tensor, paddle.float32) - 128
+        tensor[tensor > 0] /= 127.0
+        tensor[tensor < 0] /= 128.0
+    return tensor
+
+
+def get_wav_data(
+    dtype: str,
+    num_channels: int,
+    *,
+    num_frames: Optional[int] = None,
+    normalize: bool = True,
+    channels_first: bool = True,
+):
+    """Generate linear signal of the given dtype and num_channels
+
+    Data range is
+        [-1.0, 1.0] for float32,
+        [-2147483648, 2147483647] for int32
+        [-32768, 32767] for int16
+        [0, 255] for uint8
+
+    num_frames allow to change the linear interpolation parameter.
+    Default values are 256 for uint8, else 1 << 16.
+    1 << 16 as default is so that int16 value range is completely covered.
+    """
+    dtype_ = getattr(paddle, dtype)
+
+    if num_frames is None:
+        if dtype == "uint8":
+            num_frames = 256
+        else:
+            num_frames = 1 << 16
+
+    # paddle linspace not support uint8, int8, int16
+    #if dtype == "uint8":
+    #    base = paddle.linspace(0, 255, num_frames, dtype=dtype_)
+    #elif dtype == "int8":
+    #    base = paddle.linspace(-128, 127, num_frames, dtype=dtype_)
+    if dtype == "float32":
+        base = paddle.linspace(-1.0, 1.0, num_frames, dtype=dtype_)
+    elif dtype == "float64":
+        base = paddle.linspace(-1.0, 1.0, num_frames, dtype=dtype_)
+    elif dtype == "int32":
+        base = paddle.linspace(-2147483648, 2147483647, num_frames, dtype=dtype_)
+    #elif dtype == "int16":
+    #    base = paddle.linspace(-32768, 32767, num_frames, dtype=dtype_)
+    else:
+        raise NotImplementedError(f"Unsupported dtype {dtype}")
+    data = base.tile([num_channels, 1])
+    if not channels_first:
+        data = data.transpose([1, 0])
+    if normalize:
+        data = normalize_wav(data)
+    return data
+
+
+def load_wav(path: str, normalize=True, channels_first=True) -> paddle.Tensor:
+    """Load wav file without paddleaudio"""
+    sample_rate, data = scipy.io.wavfile.read(path)
+    data = paddle.to_tensor(data.copy())
+    if data.ndim == 1:
+        data = data.unsqueeze(1)
+    if normalize:
+        data = normalize_wav(data)
+    if channels_first:
+        data = data.transpose([1, 0])
+    return data, sample_rate
+
+
+def save_wav(path, data, sample_rate, channels_first=True):
+    """Save wav file without paddleaudio"""
+    if channels_first:
+        data = data.transpose([1, 0])
+    scipy.io.wavfile.write(path, sample_rate, data.numpy())