Merge pull request #629 from PaddlePaddle/align

ctc alignment

deepspeech/exps/u2/model.py

@@ -34,9 +34,12 @@ from deepspeech.models.u2 import U2Model
 from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog
 from deepspeech.training.scheduler import WarmupLR
 from deepspeech.training.trainer import Trainer
+from deepspeech.utils import ctc_utils
 from deepspeech.utils import error_rate
 from deepspeech.utils import layer_tools
 from deepspeech.utils import mp_tools
+from deepspeech.utils import text_grid
+from deepspeech.utils import utility
 from deepspeech.utils.log import Log
 
 logger = Log(__name__).getlog()
@@ -278,7 +281,15 @@ class U2Trainer(Trainer):
             shuffle=False,
             drop_last=False,
             collate_fn=SpeechCollator.from_config(config))
-        logger.info("Setup train/valid/test Dataloader!")
+        # return text token id
+        config.collator.keep_transcription_text = False
+        self.align_loader = DataLoader(
+            test_dataset,
+            batch_size=config.decoding.batch_size,
+            shuffle=False,
+            drop_last=False,
+            collate_fn=SpeechCollator.from_config(config))
+        logger.info("Setup train/valid/test/align Dataloader!")
 
     def setup_model(self):
         config = self.config
@@ -353,7 +364,7 @@ class U2Tester(U2Trainer):
                 decoding_chunk_size=-1,  # decoding chunk size. Defaults to -1.
                 # <0: for decoding, use full chunk.
                 # >0: for decoding, use fixed chunk size as set.
-                # 0: used for training, it's prohibited here. 
+                # 0: used for training, it's prohibited here.
                 num_decoding_left_chunks=-1,  # number of left chunks for decoding. Defaults to -1.
                 simulate_streaming=False,  # simulate streaming inference. Defaults to False.
             ))
@@ -498,6 +509,73 @@ class U2Tester(U2Trainer):
         except KeyboardInterrupt:
             sys.exit(-1)
 
+    @paddle.no_grad()
+    def align(self):
+        if self.config.decoding.batch_size > 1:
+            logger.fatal('alignment mode must be running with batch_size == 1')
+            sys.exit(1)
+
+        # xxx.align
+        assert self.args.result_file and self.args.result_file.endswith(
+            '.align')
+
+        self.model.eval()
+        logger.info(f"Align Total Examples: {len(self.align_loader.dataset)}")
+
+        stride_ms = self.align_loader.collate_fn.stride_ms
+        token_dict = self.align_loader.collate_fn.vocab_list
+        with open(self.args.result_file, 'w') as fout:
+            # one example in batch
+            for i, batch in enumerate(self.align_loader):
+                key, feat, feats_length, target, target_length = batch
+
+                # 1. Encoder
+                encoder_out, encoder_mask = self.model._forward_encoder(
+                    feat, feats_length)  # (B, maxlen, encoder_dim)
+                maxlen = encoder_out.size(1)
+                ctc_probs = self.model.ctc.log_softmax(
+                    encoder_out)  # (1, maxlen, vocab_size)
+
+                # 2. alignment
+                ctc_probs = ctc_probs.squeeze(0)
+                target = target.squeeze(0)
+                alignment = ctc_utils.forced_align(ctc_probs, target)
+                logger.info("align ids", key[0], alignment)
+                fout.write('{} {}\n'.format(key[0], alignment))
+
+                # 3. gen praat
+                # segment alignment
+                align_segs = text_grid.segment_alignment(alignment)
+                logger.info("align tokens", key[0], align_segs)
+                # IntervalTier, List["start end token\n"]
+                subsample = utility.get_subsample(self.config)
+                tierformat = text_grid.align_to_tierformat(
+                    align_segs, subsample, token_dict)
+                # write tier
+                align_output_path = os.path.join(
+                    os.path.dirname(self.args.result_file), "align")
+                tier_path = os.path.join(align_output_path, key[0] + ".tier")
+                with open(tier_path, 'w') as f:
+                    f.writelines(tierformat)
+                # write textgrid
+                textgrid_path = os.path.join(align_output_path,
+                                             key[0] + ".TextGrid")
+                second_per_frame = 1. / (1000. /
+                                         stride_ms)  # 25ms window, 10ms stride
+                second_per_example = (
+                    len(alignment) + 1) * subsample * second_per_frame
+                text_grid.generate_textgrid(
+                    maxtime=second_per_example,
+                    intervals=tierformat,
+                    output=textgrid_path)
+
+    def run_align(self):
+        self.resume_or_scratch()
+        try:
+            self.align()
+        except KeyboardInterrupt:
+            sys.exit(-1)
+
     def load_inferspec(self):
         """infer model and input spec.
 

deepspeech/utils/ctc_utils.py

@@ -38,21 +38,23 @@ def remove_duplicates_and_blank(hyp: List[int], blank_id=0) -> List[int]:
     new_hyp: List[int] = []
     cur = 0
     while cur < len(hyp):
+        # add non-blank into new_hyp
         if hyp[cur] != blank_id:
             new_hyp.append(hyp[cur])
+        # skip repeat label
         prev = cur
         while cur < len(hyp) and hyp[cur] == hyp[prev]:
             cur += 1
     return new_hyp
 
 
-def insert_blank(label: np.ndarray, blank_id: int=0):
+def insert_blank(label: np.ndarray, blank_id: int=0) -> np.ndarray:
     """Insert blank token between every two label token.
 
     "abcdefg" -> "-a-b-c-d-e-f-g-"
 
     Args:
-        label ([np.ndarray]): label ids, (L).
+        label ([np.ndarray]): label ids, List[int], (L).
         blank_id (int, optional): blank id. Defaults to 0.
 
     Returns:
@@ -61,13 +63,13 @@ def insert_blank(label: np.ndarray, blank_id: int=0):
     label = np.expand_dims(label, 1)  #[L, 1]
     blanks = np.zeros((label.shape[0], 1), dtype=np.int64) + blank_id
     label = np.concatenate([blanks, label], axis=1)  #[L, 2]
-    label = label.reshape(-1)  #[2L]
-    label = np.append(label, label[0])  #[2L + 1]
+    label = label.reshape(-1)  #[2L], -l-l-l
+    label = np.append(label, label[0])  #[2L + 1], -l-l-l-
     return label
 
 
 def forced_align(ctc_probs: paddle.Tensor, y: paddle.Tensor,
-                 blank_id=0) -> list:
+                 blank_id=0) -> List[int]:
     """ctc forced alignment.
 
     https://distill.pub/2017/ctc/
@@ -77,23 +79,25 @@ def forced_align(ctc_probs: paddle.Tensor, y: paddle.Tensor,
         y (paddle.Tensor): label id sequence tensor, 1d tensor (L)
         blank_id (int): blank symbol index
     Returns:
-        paddle.Tensor: best alignment result, (T).
+        List[int]: best alignment result, (T).
     """
-    y_insert_blank = insert_blank(y, blank_id)
+    y_insert_blank = insert_blank(y, blank_id)  #(2L+1)
 
     log_alpha = paddle.zeros(
         (ctc_probs.size(0), len(y_insert_blank)))  #(T, 2L+1)
     log_alpha = log_alpha - float('inf')  # log of zero
+    # TODO(Hui Zhang): zeros not support paddle.int16
     state_path = (paddle.zeros(
-        (ctc_probs.size(0), len(y_insert_blank)), dtype=paddle.int16) - 1
-                  )  # state path
+        (ctc_probs.size(0), len(y_insert_blank)), dtype=paddle.int32) - 1
+                  )  # state path, Tuple((T, 2L+1))
 
     # init start state
-    log_alpha[0, 0] = ctc_probs[0][y_insert_blank[0]]  # Sb
-    log_alpha[0, 1] = ctc_probs[0][y_insert_blank[1]]  # Snb
+    # TODO(Hui Zhang): VarBase.__getitem__() not support np.int64
+    log_alpha[0, 0] = ctc_probs[0][int(y_insert_blank[0])]  # State-b, Sb
+    log_alpha[0, 1] = ctc_probs[0][int(y_insert_blank[1])]  # State-nb, Snb
 
-    for t in range(1, ctc_probs.size(0)):
-        for s in range(len(y_insert_blank)):
+    for t in range(1, ctc_probs.size(0)):  # T
+        for s in range(len(y_insert_blank)):  # 2L+1
             if y_insert_blank[s] == blank_id or s < 2 or y_insert_blank[
                     s] == y_insert_blank[s - 2]:
                 candidates = paddle.to_tensor(
@@ -106,11 +110,13 @@ def forced_align(ctc_probs: paddle.Tensor, y: paddle.Tensor,
                     log_alpha[t - 1, s - 2],
                 ])
                 prev_state = [s, s - 1, s - 2]
-            log_alpha[t, s] = paddle.max(candidates) + ctc_probs[t][
-                y_insert_blank[s]]
+            # TODO(Hui Zhang): VarBase.__getitem__() not support np.int64
+            log_alpha[t, s] = paddle.max(candidates) + ctc_probs[t][int(
+                y_insert_blank[s])]
             state_path[t, s] = prev_state[paddle.argmax(candidates)]
 
-    state_seq = -1 * paddle.ones((ctc_probs.size(0), 1), dtype=paddle.int16)
+    # TODO(Hui Zhang): zeros not support paddle.int16
+    state_seq = -1 * paddle.ones((ctc_probs.size(0), 1), dtype=paddle.int32)
 
     candidates = paddle.to_tensor([
         log_alpha[-1, len(y_insert_blank) - 1],  # Sb

deepspeech/utils/text_grid.py

+# Copyright (c) 2021 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.
+from typing import Dict
+from typing import List
+from typing import Text
+
+import textgrid
+
+
+def segment_alignment(alignment: List[int], blank_id=0) -> List[List[int]]:
+    """segment ctc alignment ids by continuous blank and repeat label.
+
+    Args:
+        alignment (List[int]): ctc alignment id sequence. 
+            e.g. [0, 0, 0, 1, 1, 1, 2, 0, 0, 3]
+        blank_id (int, optional): blank id. Defaults to 0.
+
+    Returns:
+        List[List[int]]: token align, segment aligment id sequence. 
+            e.g. [[0, 0, 0, 1, 1, 1], [2], [0, 0, 3]]
+    """
+    # convert alignment to a praat format, which is a doing phonetics
+    # by computer and helps analyzing alignment
+    align_segs = []
+    # get frames level duration for each token
+    start = 0
+    end = 0
+    while end < len(alignment):
+        while end < len(alignment) and alignment[end] == blank_id:  # blank
+            end += 1
+        if end == len(alignment):
+            align_segs[-1].extend(alignment[start:])
+            break
+        end += 1
+        while end < len(alignment) and alignment[end - 1] == alignment[
+                end]:  # repeat label
+            end += 1
+        align_segs.append(alignment[start:end])
+        start = end
+    return align_segs
+
+
+def align_to_tierformat(align_segs: List[List[int]],
+                        subsample: int,
+                        token_dict: Dict[int, Text],
+                        blank_id=0) -> List[Text]:
+    """Generate textgrid.Interval format from alignment segmentations.
+
+    Args:
+        align_segs (List[List[int]]): segmented ctc alignment ids.
+        subsample (int): 25ms frame_length, 10ms hop_length, 1/subsample
+        token_dict (Dict[int, Text]): int -> str map.
+
+    Returns:
+        List[Text]: list of textgrid.Interval text, str(start, end, text).
+    """
+    hop_length = 10  # ms
+    second_ms = 1000  # ms
+    frame_per_second = second_ms / hop_length  # 25ms frame_length, 10ms hop_length
+    second_per_frame = 1.0 / frame_per_second
+
+    begin = 0
+    duration = 0
+    tierformat = []
+
+    for idx, tokens in enumerate(align_segs):
+        token_len = len(tokens)
+        token = tokens[-1]
+        # time duration in second
+        duration = token_len * subsample * second_per_frame
+        if idx < len(align_segs) - 1:
+            print(f"{begin:.2f} {begin + duration:.2f} {token_dict[token]}")
+            tierformat.append(
+                f"{begin:.2f} {begin + duration:.2f} {token_dict[token]}\n")
+        else:
+            for i in tokens:
+                if i != blank_id:
+                    token = i
+                    break
+            print(f"{begin:.2f} {begin + duration:.2f} {token_dict[token]}")
+            tierformat.append(
+                f"{begin:.2f} {begin + duration:.2f} {token_dict[token]}\n")
+        begin = begin + duration
+
+    return tierformat
+
+
+def generate_textgrid(maxtime: float,
+                      intervals: List[Text],
+                      output: Text,
+                      name: Text='ali') -> None:
+    """Create alignment textgrid file.
+
+    Args:
+        maxtime (float): audio duartion.
+        intervals (List[Text]): ctc output alignment. e.g. "start-time end-time word" per item.
+        output (Text): textgrid filepath.
+        name (Text, optional): tier or layer name. Defaults to 'ali'.
+    """
+    # Download Praat: https://www.fon.hum.uva.nl/praat/
+    avg_interval = maxtime / (len(intervals) + 1)
+    print(f"average second/token: {avg_interval}")
+    margin = 0.0001
+
+    tg = textgrid.TextGrid(maxTime=maxtime)
+    tier = textgrid.IntervalTier(name=name, maxTime=maxtime)
+
+    i = 0
+    for dur in intervals:
+        s, e, text = dur.split()
+        tier.add(minTime=float(s) + margin, maxTime=float(e), mark=text)
+
+    tg.append(tier)
+
+    tg.write(output)
+    print("successfully generator textgrid {}.".format(output))

deepspeech/utils/utility.py

@@ -79,3 +79,22 @@ def log_add(args: List[int]) -> float:
     a_max = max(args)
     lsp = math.log(sum(math.exp(a - a_max) for a in args))
     return a_max + lsp
+
+
+def get_subsample(config):
+    """Subsample rate from config.
+
+    Args:
+        config (yacs.config.CfgNode): yaml config
+
+    Returns:
+        int: subsample rate.
+    """
+    input_layer = config["model"]["encoder_conf"]["input_layer"]
+    assert input_layer in ["conv2d", "conv2d6", "conv2d8"]
+    if input_layer == "conv2d":
+        return 4
+    elif input_layer == "conv2d6":
+        return 6
+    elif input_layer == "conv2d8":
+        return 8

examples/aishell/s1/local/align.sh

+#! /usr/bin/env bash
+
+if [ $# != 2 ];then
+    echo "usage: ${0} config_path ckpt_path_prefix"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+device=gpu
+if [ ngpu == 0 ];then
+    device=cpu
+fi
+config_path=$1
+ckpt_prefix=$2
+
+ckpt_name=$(basename ${ckpt_prefxi})
+
+mkdir -p exp
+
+
+
+batch_size=1
+output_dir=${ckpt_prefix}
+mkdir -p ${output_dir}
+
+# align dump in `result_file`
+# .tier, .TextGrid dump in `dir of result_file`
+python3 -u ${BIN_DIR}/alignment.py \
+--device ${device} \
+--nproc 1 \
+--config ${config_path} \
+--result_file ${output_dir}/${type}.align \
+--checkpoint_path ${ckpt_prefix} \
+--opts decoding.batch_size ${batch_size}
+
+if [ $? -ne 0 ]; then
+    echo "Failed in ctc alignment!"
+    exit 1
+fi
+
+exit 0

examples/aishell/s1/run.sh

@@ -30,10 +30,15 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=4 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    # ctc alignment of test data
+    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+fi
+
+if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # export ckpt avg_n
     CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
 fi

examples/librispeech/s1/local/align.sh

+#! /usr/bin/env bash
+
+if [ $# != 2 ];then
+    echo "usage: ${0} config_path ckpt_path_prefix"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+device=gpu
+if [ ngpu == 0 ];then
+    device=cpu
+fi
+config_path=$1
+ckpt_prefix=$2
+
+ckpt_name=$(basename ${ckpt_prefxi})
+
+mkdir -p exp
+
+
+
+batch_size=1
+output_dir=${ckpt_prefix}
+mkdir -p ${output_dir}
+
+# align dump in `result_file`
+# .tier, .TextGrid dump in `dir of result_file`
+python3 -u ${BIN_DIR}/alignment.py \
+--device ${device} \
+--nproc 1 \
+--config ${config_path} \
+--result_file ${output_dir}/${type}.align \
+--checkpoint_path ${ckpt_prefix} \
+--opts decoding.batch_size ${batch_size}
+
+if [ $? -ne 0 ]; then
+    echo "Failed in ctc alignment!"
+    exit 1
+fi
+
+exit 0

examples/librispeech/s1/run.sh

@@ -33,6 +33,11 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    # ctc alignment of test data
+    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+fi
+
+if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # export ckpt avg_n
     CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
 fi

examples/tiny/s1/local/align.sh

+#! /usr/bin/env bash
+
+if [ $# != 2 ];then
+    echo "usage: ${0} config_path ckpt_path_prefix"
+    exit -1
+fi
+
+ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+echo "using $ngpu gpus..."
+
+device=gpu
+if [ ngpu == 0 ];then
+    device=cpu
+fi
+config_path=$1
+ckpt_prefix=$2
+
+ckpt_name=$(basename ${ckpt_prefxi})
+
+mkdir -p exp
+
+
+
+batch_size=1
+output_dir=${ckpt_prefix}
+mkdir -p ${output_dir}
+
+# align dump in `result_file`
+# .tier, .TextGrid dump in `dir of result_file`
+python3 -u ${BIN_DIR}/alignment.py \
+--device ${device} \
+--nproc 1 \
+--config ${config_path} \
+--result_file ${output_dir}/${type}.align \
+--checkpoint_path ${ckpt_prefix} \
+--opts decoding.batch_size ${batch_size}
+
+if [ $? -ne 0 ]; then
+    echo "Failed in ctc alignment!"
+    exit 1
+fi
+
+exit 0

examples/tiny/s1/run.sh

@@ -34,6 +34,12 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    # ctc alignment of test data
+    CUDA_VISIBLE_DEVICES=0 ./local/align.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+fi
+
+if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
     # export ckpt avg_n
-    CUDA_VISIBLE_DEVICES=  ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
+    CUDA_VISIBLE_DEVICES= ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
 fi
+

tools/Makefile

@@ -19,7 +19,7 @@ kenlm.done:
 	apt-get install -y gcc-5 g++-5 && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 50  && update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 50
 	test -d kenlm || wget -O - https://kheafield.com/code/kenlm.tar.gz | tar xz
 	mkdir -p kenlm/build && cd kenlm/build && cmake .. && make -j4 && make install
-	cd kenlm && python setup.py install
+	source venv/bin/activate; cd kenlm && python setup.py install
 	touch kenlm.done
 
 sox.done:
@@ -32,4 +32,4 @@ sox.done:
 soxbindings.done:
 	test -d soxbindings || git clone https://github.com/pseeth/soxbindings.git
 	source venv/bin/activate; cd soxbindings && python setup.py install
-	touch soxbindings.done
+	touch soxbindings.done
\ No newline at end of file