add feature pipeline layer(cmvn, fbank), but to_static and jit.layer output is not equal

paddlespeech/audio/compliance/kaldi.py

@@ -74,16 +74,16 @@ def _feature_window_function(
         window_size: int,
         blackman_coeff: float,
         dtype: int, ) -> Tensor:
-    if window_type == HANNING:
+    if window_type == "hann":
         return get_window('hann', window_size, fftbins=False, dtype=dtype)
-    elif window_type == HAMMING:
+    elif window_type == "hamming":
         return get_window('hamming', window_size, fftbins=False, dtype=dtype)
-    elif window_type == POVEY:
+    elif window_type == "povey":
         return get_window(
             'hann', window_size, fftbins=False, dtype=dtype).pow(0.85)
-    elif window_type == RECTANGULAR:
+    elif window_type == "rect":
         return paddle.ones([window_size], dtype=dtype)
-    elif window_type == BLACKMAN:
+    elif window_type == "blackman":
         a = 2 * math.pi / (window_size - 1)
         window_function = paddle.arange(window_size, dtype=dtype)
         return (blackman_coeff - 0.5 * paddle.cos(a * window_function) +
@@ -216,7 +216,7 @@ def spectrogram(waveform: Tensor,
                 sr: int=16000,
                 snip_edges: bool=True,
                 subtract_mean: bool=False,
-                window_type: str=POVEY) -> Tensor:
+                window_type: str="povey") -> Tensor:
     """Compute and return a spectrogram from a waveform. The output is identical to Kaldi's.
 
     Args:
@@ -236,7 +236,7 @@ def spectrogram(waveform: Tensor,
         snip_edges (bool, optional): Drop samples in the end of waveform that cann't fit a singal frame when it
             is set True. Otherwise performs reflect padding to the end of waveform. Defaults to True.
         subtract_mean (bool, optional): Whether to subtract mean of feature files. Defaults to False.
-        window_type (str, optional): Choose type of window for FFT computation. Defaults to POVEY.
+        window_type (str, optional): Choose type of window for FFT computation. Defaults to "povey".
 
     Returns:
         Tensor: A spectrogram tensor with shape `(m, padded_window_size // 2 + 1)` where m is the number of frames
@@ -418,11 +418,11 @@ def fbank(waveform: Tensor,
           vtln_high: float=-500.0,
           vtln_low: float=100.0,
           vtln_warp: float=1.0,
-          window_type: str=POVEY) -> Tensor:
+          window_type: str="povey") -> Tensor:
     """Compute and return filter banks from a waveform. The output is identical to Kaldi's.
 
     Args:
-        waveform (Tensor): A waveform tensor with shape `(C, T)`.
+        waveform (Tensor): A waveform tensor with shape `(C, T)`. `C` is in the range [0,1]. 
         blackman_coeff (float, optional): Coefficient for Blackman window.. Defaults to 0.42.
         channel (int, optional): Select the channel of waveform. Defaults to -1.
         dither (float, optional): Dithering constant . Defaults to 0.0.
@@ -448,7 +448,7 @@ def fbank(waveform: Tensor,
         vtln_high (float, optional): High inflection point in piecewise linear VTLN warping function. Defaults to -500.0.
         vtln_low (float, optional): Low inflection point in piecewise linear VTLN warping function. Defaults to 100.0.
         vtln_warp (float, optional): Vtln warp factor. Defaults to 1.0.
-        window_type (str, optional): Choose type of window for FFT computation. Defaults to POVEY.
+        window_type (str, optional): Choose type of window for FFT computation. Defaults to "povey".
 
     Returns:
         Tensor: A filter banks tensor with shape `(m, n_mels)`.
@@ -537,7 +537,7 @@ def mfcc(waveform: Tensor,
          vtln_high: float=-500.0,
          vtln_low: float=100.0,
          vtln_warp: float=1.0,
-         window_type: str=POVEY) -> Tensor:
+         window_type: str="povey") -> Tensor:
     """Compute and return mel frequency cepstral coefficients from a waveform. The output is
             identical to Kaldi's.
 

paddlespeech/s2t/exps/u2/bin/test_wav.py

@@ -18,6 +18,7 @@ from pathlib import Path
 
 import paddle
 import soundfile
+import numpy as np
 from yacs.config import CfgNode
 
 from paddlespeech.audio.transform.transformation import Transformation
@@ -77,6 +78,8 @@ class U2Infer():
             feat = self.preprocessing(audio, **self.preprocess_args)
             logger.info(f"feat shape: {feat.shape}")
 
+            np.savetxt("feat.transform.txt", feat)
+
             ilen = paddle.to_tensor(feat.shape[0])
             xs = paddle.to_tensor(feat, dtype='float32').unsqueeze(axis=0)
             decode_config = self.config.decode

paddlespeech/s2t/exps/u2/model.py

@@ -474,13 +474,20 @@ class U2Tester(U2Trainer):
     def export(self):
         infer_model, input_spec = self.load_inferspec()
         infer_model.eval()
+        paddle.set_device('cpu')
 
-        assert isinstance(input_spec, list), type(input_spec)
+        assert isinstance(input_spec, (list, tuple)), type(input_spec)
         batch_size, feat_dim, model_size, num_left_chunks = input_spec
 
 
-        ######################### infer_model.forward_encoder_chunk zero tensor online ############
-        # TODO: 80(feature dim) be configable
+        ######################## infer_model.forward_encoder_chunk ############
+        input_spec = [
+            # (T,), int16
+            paddle.static.InputSpec(shape=[None], dtype='int16'),
+        ]
+        infer_model.forward_feature = paddle.jit.to_static(infer_model.forward_feature, input_spec=input_spec)
+
+        ######################### infer_model.forward_encoder_chunk ############
         input_spec = [
             # xs, (B, T, D)
             paddle.static.InputSpec(shape=[batch_size, None, feat_dim], dtype='float32'),
@@ -499,8 +506,16 @@ class U2Tester(U2Trainer):
         infer_model.forward_encoder_chunk = paddle.jit.to_static(
             infer_model.forward_encoder_chunk, input_spec=input_spec)
 
+        ######################### infer_model.ctc_activation ########################
+        input_spec = [
+            # encoder_out, (B,T,D)
+            paddle.static.InputSpec(shape=[batch_size, None, model_size], dtype='float32')
+        ]
+        infer_model.ctc_activation = paddle.jit.to_static(
+            infer_model.ctc_activation, input_spec=input_spec)
+
+
         ######################### infer_model.forward_attention_decoder ########################
-        # TODO: 512(encoder_output) be configable. 1 for BatchSize
         input_spec = [
             # hyps, (B, U)
             paddle.static.InputSpec(shape=[None, None], dtype='int64'),
@@ -512,17 +527,11 @@ class U2Tester(U2Trainer):
         infer_model.forward_attention_decoder = paddle.jit.to_static(
             infer_model.forward_attention_decoder, input_spec=input_spec)
 
-        ######################### infer_model.ctc_activation ########################
-        input_spec = [
-            # encoder_out, (B,T,D)
-            paddle.static.InputSpec(shape=[batch_size, None, model_size], dtype='float32')
-        ]
-        infer_model.ctc_activation = paddle.jit.to_static(
-            infer_model.ctc_activation, input_spec=input_spec)
-
         # jit save
+        logger.info(f"export save: {self.args.export_path}")
         paddle.jit.save(infer_model, self.args.export_path, combine_params=True, skip_forward=True)
 
+
         # test dy2static
         def flatten(out):
             if isinstance(out, paddle.Tensor):
@@ -536,26 +545,44 @@ class U2Tester(U2Trainer):
                     flatten_out.append(var)
             return flatten_out
 
-        xs1 = paddle.rand(shape=[1, 67, 80], dtype='float32')
+        # forward_encoder_chunk dygraph
+        xs1 = paddle.full([1, 67, 80], 0.1, dtype='float32')
         offset = paddle.to_tensor([0], dtype='int32')
         required_cache_size = num_left_chunks
         att_cache = paddle.zeros([0, 0, 0, 0])
         cnn_cache = paddle.zeros([0, 0, 0, 0])
-
-        xs, att_cache, cnn_cache = infer_model.forward_encoder_chunk(xs1, offset, required_cache_size, att_cache, cnn_cache)
-        xs2 = paddle.rand(shape=[1, 67, 80], dtype='float32')
-        offset = paddle.to_tensor([16], dtype='int32')
-        out1 = infer_model.forward_encoder_chunk(xs2, offset, required_cache_size, att_cache, cnn_cache)
-        print('py encoder', out1)
-
+        xs_d, att_cache_d, cnn_cache_d = infer_model.forward_encoder_chunk(xs1, offset, required_cache_size, att_cache, cnn_cache)
+
+        import soundfile
+        audio, sample_rate = soundfile.read(
+            './zh.wav', dtype="int16", always_2d=True)
+        audio = audio[:, 0]
+        logger.info(f"audio shape: {audio.shape}")
+        audio = paddle.to_tensor(audio, paddle.int16)
+        feat_d = infer_model.forward_feature(audio)
+        logger.info(f"{feat_d}")
+        np.savetxt("feat.tostatic.txt", feat_d)
+        
+
+        # load static model
         from paddle.jit.layer import Layer
         layer = Layer()
         layer.load(self.args.export_path, paddle.CPUPlace())
 
-        xs1 = paddle.full([1, 7, 80], 0.1, dtype='float32')
+        # forward_encoder_chunk static
+        xs1 = paddle.full([1, 67, 80], 0.1, dtype='float32')
         offset = paddle.to_tensor([0], dtype='int32')
         att_cache = paddle.zeros([0, 0, 0, 0])
-        cnn_cache=paddle.zeros([0, 0, 0, 0])
+        cnn_cache = paddle.zeros([0, 0, 0, 0])
         func = getattr(layer, 'forward_encoder_chunk')
-        xs, att_cache, cnn_cache = func(xs1, offset, att_cache, cnn_cache)
-        print('py static encoder', xs)
+        xs_s, att_cache_s, cnn_cache_s = func(xs1, offset, att_cache, cnn_cache)
+        np.testing.assert_allclose(xs_d, xs_s, atol=1e-5)
+        np.testing.assert_allclose(att_cache_d, att_cache_s, atol=1e-4)
+        np.testing.assert_allclose(cnn_cache_d, cnn_cache_s, atol=1e-4)
+        # logger.info(f"forward_encoder_chunk output: {xs_s}")
+
+        # forward_feature static
+        func = getattr(layer, 'forward_feature')
+        feat_s = func(audio)[0]
+        logger.info(f"{feat_s}")
+        np.testing.assert_allclose(feat_d, feat_s, atol=1e-5)

paddlespeech/s2t/models/u2/u2.py

@@ -916,6 +916,50 @@ class U2InferModel(U2Model):
     def __init__(self, configs: dict):
         super().__init__(configs)
 
+        from paddlespeech.s2t.modules.fbank import KaldiFbank
+        import yaml
+        import json
+        import numpy as np
+
+        input_dim = configs['input_dim']
+        process = configs['preprocess_config']
+        with open(process, encoding="utf-8") as f:
+            conf = yaml.safe_load(f)
+            assert isinstance(conf, dict), type(self.conf)
+
+        for idx, process in enumerate(conf['process']):
+            assert isinstance(process, dict), type(process)
+            opts = dict(process)
+            process_type = opts.pop("type")
+
+            if process_type == 'fbank_kaldi':
+                opts.update({'n_mels': input_dim})
+                opts['dither'] = 0.0
+                self.fbank = KaldiFbank(
+                   **opts
+                )
+                logger.info(f"{self.__class__.__name__} export: {self.fbank}")
+            if process_type == 'cmvn_json':
+                # align with paddlespeech.audio.transform.cmvn:GlobalCMVN
+                std_floor = 1.0e-20
+
+                cmvn = opts['cmvn_path']
+                if isinstance(cmvn, dict):
+                    cmvn_stats = cmvn
+                else:
+                    with open(cmvn) as f:
+                        cmvn_stats = json.load(f)
+                count = cmvn_stats['frame_num']
+                mean = np.array(cmvn_stats['mean_stat']) / count
+                square_sums = np.array(cmvn_stats['var_stat'])
+                var = square_sums / count - mean**2
+                std = np.maximum(np.sqrt(var), std_floor)
+                istd = 1.0 / std
+                self.global_cmvn = GlobalCMVN(
+                    paddle.to_tensor(mean, dtype=paddle.float),
+                    paddle.to_tensor(istd, dtype=paddle.float))
+                logger.info(f"{self.__class__.__name__} export: {self.global_cmvn}")
+
     def forward(self,
                 feats,
                 feats_lengths,
@@ -939,3 +983,17 @@ class U2InferModel(U2Model):
         #     num_decoding_left_chunks=num_decoding_left_chunks,
         #     simulate_streaming=simulate_streaming)
         return feats, feats_lengths
+
+    def forward_feature(self, x):
+        """feature pipeline.
+
+        Args:
+            x (paddle.Tensor): waveform (T,).
+
+        Return:
+            feat (paddle.Tensor): feature (T, D) 
+        """
+        x = paddle.cast(x, paddle.float32)
+        feat = self.fbank(x)
+        feat = self.global_cmvn(feat)
+        return feat
\ No newline at end of file

paddlespeech/s2t/modules/cmvn.py

@@ -40,6 +40,14 @@ class GlobalCMVN(nn.Layer):
         self.register_buffer("mean", mean)
         self.register_buffer("istd", istd)
 
+    def __repr__(self):
+        return (
+            "{name}(mean={mean}, istd={istd}, norm_var={norm_var})".format(
+                name=self.__class__.__name__,
+                mean=self.mean,
+                istd=self.istd,
+                norm_var=self.norm_var))
+
     def forward(self, x: paddle.Tensor):
         """
         Args:
@@ -50,4 +58,4 @@ class GlobalCMVN(nn.Layer):
         x = x - self.mean
         if self.norm_var:
             x = x * self.istd
-        return x
+        return x
\ No newline at end of file

paddlespeech/s2t/modules/fbank.py

+
+
+
+import paddle
+from paddle import nn
+
+from paddlespeech.audio.compliance import kaldi
+
+from paddlespeech.s2t.utils.log import Log
+
+logger = Log(__name__).getlog()
+
+__all__ = ['KaldiFbank']
+
+class KaldiFbank(nn.Layer):
+    def __init__(self,
+            fs=16000,
+            n_mels=80,
+            n_shift=160,  # unit:sample, 10ms
+            win_length=400,  # unit:sample, 25ms
+            energy_floor=0.0,
+            dither=0.0):
+        """
+        Args:
+            fs (int): sample rate of the audio
+            n_mels (int): number of mel filter banks
+            n_shift (int): number of points in a frame shift
+            win_length (int): number of points in a frame windows
+            energy_floor (float): Floor on energy in Spectrogram computation (absolute)
+            dither (float): Dithering constant. Default 0.0
+        """
+        super().__init__()
+        self.fs = fs
+        self.n_mels = n_mels
+        num_point_ms = fs / 1000
+        self.n_frame_length = win_length / num_point_ms
+        self.n_frame_shift = n_shift / num_point_ms
+        self.energy_floor = energy_floor
+        self.dither = dither
+
+    def __repr__(self):
+        return (
+            "{name}(fs={fs}, n_mels={n_mels}, "
+            "n_frame_shift={n_frame_shift}, n_frame_length={n_frame_length}, "
+            "dither={dither}))".format(
+                name=self.__class__.__name__,
+                fs=self.fs,
+                n_mels=self.n_mels,
+                n_frame_shift=self.n_frame_shift,
+                n_frame_length=self.n_frame_length,
+                dither=self.dither, ))
+
+    def forward(self, x: paddle.Tensor):
+        """
+        Args:
+            x (paddle.Tensor): shape (Ti). 
+                Not support: [Time, Channel] and Batch mode.
+
+        Returns:
+            paddle.Tensor: (T, D)
+        """
+        assert x.ndim == 1
+
+        feat = kaldi.fbank(
+            x.unsqueeze(0), # append channel dim, (C, Ti)
+            n_mels=self.n_mels,
+            frame_length=self.n_frame_length,
+            frame_shift=self.n_frame_shift,
+            dither=self.dither,
+            energy_floor=self.energy_floor,
+            sr=self.fs)
+
+        assert feat.ndim == 2 # (T,D)
+        return feat