add conformer ctc prefix beam search decoding method, test=doc

paddlespeech/server/engine/asr/online/asr_engine.py

@@ -12,8 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
+from paddlespeech.s2t.utils.utility import log_add
 from typing import Optional
-
+from collections import defaultdict
 import numpy as np
 import paddle
 from numpy import float32
@@ -23,10 +24,14 @@ from paddlespeech.cli.asr.infer import ASRExecutor
 from paddlespeech.cli.asr.infer import model_alias
 from paddlespeech.cli.asr.infer import pretrained_models
 from paddlespeech.cli.log import logger
+from paddlespeech.cli.utils import download_and_decompress
 from paddlespeech.cli.utils import MODEL_HOME
 from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.frontend.speech import SpeechSegment
 from paddlespeech.s2t.modules.ctc import CTCDecoder
+from paddlespeech.s2t.modules.mask import mask_finished_preds
+from paddlespeech.s2t.modules.mask import mask_finished_scores
+from paddlespeech.s2t.modules.mask import subsequent_mask
 from paddlespeech.s2t.transform.transformation import Transformation
 from paddlespeech.s2t.utils.dynamic_import import dynamic_import
 from paddlespeech.s2t.utils.utility import UpdateConfig
@@ -57,17 +62,17 @@ pretrained_models = {
     },
     "conformer2online_aishell-zh-16k": {
         'url':
-        'https://paddlespeech.bj.bcebos.com/s2t/aishell/asr0/asr1_chunk_conformer_aishell_ckpt_0.1.2.model.tar.gz',
+        'https://paddlespeech.bj.bcebos.com/s2t/multi_cn/asr1/asr1_chunk_conformer_multi_cn_ckpt_0.2.0.model.tar.gz',
         'md5':
-        '4814e52e0fc2fd48899373f95c84b0c9',
+        '7989b3248c898070904cf042fd656003',
         'cfg_path':
-        'exp/chunk_conformer//conf/config.yaml',
+        'model.yaml',
         'ckpt_path':
-        'exp/chunk_conformer/checkpoints/avg_30/',
+        'exp/chunk_conformer/checkpoints/multi_cn',
         'model':
-        'exp/chunk_conformer/checkpoints/avg_30.pdparams',
+        'exp/chunk_conformer/checkpoints/multi_cn.pdparams',
         'params':
-        'exp/chunk_conformer/checkpoints/avg_30.pdparams',
+        'exp/chunk_conformer/checkpoints/multi_cn.pdparams',
         'lm_url':
         'https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm',
         'lm_md5':
@@ -81,6 +86,23 @@ class ASRServerExecutor(ASRExecutor):
         super().__init__()
         pass
 
+    def _get_pretrained_path(self, tag: str) -> os.PathLike:
+        """
+        Download and returns pretrained resources path of current task.
+        """
+        support_models = list(pretrained_models.keys())
+        assert tag in pretrained_models, 'The model "{}" you want to use has not been supported, please choose other models.\nThe support models includes:\n\t\t{}\n'.format(
+            tag, '\n\t\t'.join(support_models))
+
+        res_path = os.path.join(MODEL_HOME, tag)
+        decompressed_path = download_and_decompress(pretrained_models[tag],
+                                                    res_path)
+        decompressed_path = os.path.abspath(decompressed_path)
+        logger.info(
+            'Use pretrained model stored in: {}'.format(decompressed_path))
+
+        return decompressed_path
+
     def _init_from_path(self,
                         model_type: str='wenetspeech',
                         am_model: Optional[os.PathLike]=None,
@@ -101,7 +123,7 @@ class ASRServerExecutor(ASRExecutor):
             logger.info(f"Load the pretrained model, tag = {tag}")
             res_path = self._get_pretrained_path(tag)  # wenetspeech_zh
             self.res_path = res_path
-            self.cfg_path = "/home/users/xiongxinlei/task/paddlespeech-develop/PaddleSpeech/paddlespeech/server/tests/asr/online/conf/config.yaml"
+            self.cfg_path = "/home/users/xiongxinlei/task/paddlespeech-develop/PaddleSpeech/examples/aishell/asr1/model.yaml"
             # self.cfg_path = os.path.join(res_path,
             #                              pretrained_models[tag]['cfg_path'])
 
@@ -147,8 +169,7 @@ class ASRServerExecutor(ASRExecutor):
                 if self.config.spm_model_prefix:
                     self.config.spm_model_prefix = os.path.join(
                         self.res_path, self.config.spm_model_prefix)
-                self.config.vocab_filepath = os.path.join(
+                self.vocab = self.config.vocab_filepath
-                    self.res_path, self.config.vocab_filepath)
                 self.text_feature = TextFeaturizer(
                     unit_type=self.config.unit_type,
                     vocab=self.config.vocab_filepath,
@@ -203,11 +224,21 @@ class ASRServerExecutor(ASRExecutor):
             model_conf = self.config
             model = model_class.from_config(model_conf)
             self.model = model
+            self.model.eval()
+
+            # load model
+            model_dict = paddle.load(self.am_model)
+            self.model.set_state_dict(model_dict)
             logger.info("create the transformer like model success")
 
+            # update the ctc decoding
+            self.searcher = None
+            self.transformer_decode_reset()
+
     def reset_decoder_and_chunk(self):
         """reset decoder and chunk state for an new audio
         """
+        if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type:
             self.decoder.reset_decoder(batch_size=1)
             # init state box, for new audio request
             self.chunk_state_h_box = np.zeros(
@@ -216,6 +247,8 @@ class ASRServerExecutor(ASRExecutor):
             self.chunk_state_c_box = np.zeros(
                 (self.config.num_rnn_layers, 1, self.config.rnn_layer_size),
                 dtype=float32)
+        elif "conformer" in self.model_type or "transformer" in self.model_type or "wenetspeech" in self.model_type:
+            self.transformer_decode_reset()
 
     def decode_one_chunk(self, x_chunk, x_chunk_lens, model_type: str):
         """decode one chunk
@@ -275,24 +308,137 @@ class ASRServerExecutor(ASRExecutor):
                 logger.info(
                     f"we will use the transformer like model : {self.model_type}"
                 )
-                cfg = self.config.decode
+                self.advanced_decoding(x_chunk, x_chunk_lens)
-                result_transcripts = self.model.decode(
+                self.update_result()
-                    x_chunk,
+
-                    x_chunk_lens,
+                return self.result_transcripts[0]
-                    text_feature=self.text_feature,
-                    decoding_method=cfg.decoding_method,
-                    beam_size=cfg.beam_size,
-                    ctc_weight=cfg.ctc_weight,
-                    decoding_chunk_size=cfg.decoding_chunk_size,
-                    num_decoding_left_chunks=cfg.num_decoding_left_chunks,
-                    simulate_streaming=cfg.simulate_streaming)
-
-                return result_transcripts[0][0]
             except Exception as e:
                 logger.exception(e)
         else:
             raise Exception("invalid model name")
 
+    def advanced_decoding(self, xs: paddle.Tensor, x_chunk_lens):
+        logger.info("start to decode with advanced_decoding method")
+        encoder_out, encoder_mask = self.decode_forward(xs)
+        self.ctc_prefix_beam_search(xs, encoder_out, encoder_mask)
+
+    def decode_forward(self, xs):
+        logger.info("get the model out from the feat")
+        cfg = self.config.decode
+        decoding_chunk_size = cfg.decoding_chunk_size
+        num_decoding_left_chunks = cfg.num_decoding_left_chunks
+
+        assert decoding_chunk_size > 0
+        subsampling = self.model.encoder.embed.subsampling_rate
+        context = self.model.encoder.embed.right_context + 1
+        stride = subsampling * decoding_chunk_size
+
+        # decoding window for model
+        decoding_window = (decoding_chunk_size - 1) * subsampling + context
+        num_frames = xs.shape[1]
+        required_cache_size = decoding_chunk_size * num_decoding_left_chunks
+
+
+        logger.info("start to do model forward")
+        outputs = []
+
+        # num_frames - context + 1 ensure that current frame can get context window
+        for cur in range(0, num_frames - context + 1, stride):
+            end = min(cur + decoding_window, num_frames)
+            chunk_xs = xs[:, cur:end, :]
+            (y, self.subsampling_cache, self.elayers_output_cache,
+             self.conformer_cnn_cache) = self.model.encoder.forward_chunk(
+                 chunk_xs, self.offset, required_cache_size,
+                 self.subsampling_cache, self.elayers_output_cache,
+                 self.conformer_cnn_cache)
+            outputs.append(y)
+            self.offset += y.shape[1]
+
+        ys = paddle.cat(outputs, 1)
+        masks = paddle.ones([1, ys.shape[1]], dtype=paddle.bool)
+        masks = masks.unsqueeze(1)
+        return ys, masks
+
+    def transformer_decode_reset(self):
+        self.subsampling_cache = None
+        self.elayers_output_cache = None
+        self.conformer_cnn_cache = None
+        self.hyps = None
+        self.offset = 0
+        self.cur_hyps = None
+        self.hyps = None
+
+    def ctc_prefix_beam_search(self, xs, encoder_out, encoder_mask, blank_id=0):
+        # decode 
+        logger.info("start to ctc prefix search")
+
+        device = xs.place
+        cfg = self.config.decode
+        batch_size = xs.shape[0]
+        beam_size = cfg.beam_size
+        maxlen = encoder_out.shape[1]
+
+        ctc_probs = self.model.ctc.log_softmax(encoder_out)  # (1, maxlen, vocab_size)
+        ctc_probs = ctc_probs.squeeze(0)
+        
+        # cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score))
+        # blank_ending_score and  none_blank_ending_score in ln domain
+        if self.cur_hyps is None:
+            self.cur_hyps = [(tuple(), (0.0, -float('inf')))]
+        # 2. CTC beam search step by step
+        for t in range(0, maxlen):
+            logp = ctc_probs[t]  # (vocab_size,)
+            # key: prefix, value (pb, pnb), default value(-inf, -inf)
+            next_hyps = defaultdict(lambda: (-float('inf'), -float('inf')))
+            
+            # 2.1 First beam prune: select topk best
+            #     do token passing process
+            top_k_logp, top_k_index = logp.topk(beam_size)  # (beam_size,)
+            for s in top_k_index:
+                s = s.item()
+                ps = logp[s].item()
+                for prefix, (pb, pnb) in self.cur_hyps:
+                    last = prefix[-1] if len(prefix) > 0 else None
+                    if s == blank_id:  # blank
+                        n_pb, n_pnb = next_hyps[prefix]
+                        n_pb = log_add([n_pb, pb + ps, pnb + ps])
+                        next_hyps[prefix] = (n_pb, n_pnb)
+                    elif s == last:
+                        #  Update *ss -> *s;
+                        n_pb, n_pnb = next_hyps[prefix]
+                        n_pnb = log_add([n_pnb, pnb + ps])
+                        next_hyps[prefix] = (n_pb, n_pnb)
+                        # Update *s-s -> *ss, - is for blank
+                        n_prefix = prefix + (s, )
+                        n_pb, n_pnb = next_hyps[n_prefix]
+                        n_pnb = log_add([n_pnb, pb + ps])
+                        next_hyps[n_prefix] = (n_pb, n_pnb)
+                    else:
+                        n_prefix = prefix + (s, )
+                        n_pb, n_pnb = next_hyps[n_prefix]
+                        n_pnb = log_add([n_pnb, pb + ps, pnb + ps])
+                        next_hyps[n_prefix] = (n_pb, n_pnb)
+
+            # 2.2 Second beam prune
+            next_hyps = sorted(
+                next_hyps.items(),
+                key=lambda x: log_add(list(x[1])),
+                reverse=True)
+            self.cur_hyps = next_hyps[:beam_size]
+
+        hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in self.cur_hyps]
+        
+        self.hyps = [hyps[0][0]]
+        logger.info("ctc prefix search success")
+        return hyps, encoder_out
+
+    def update_result(self):
+        logger.info("update the final result")
+        self.result_transcripts = [
+            self.text_feature.defeaturize(hyp) for hyp in self.hyps
+        ]
+        self.result_tokenids = [hyp for hyp in self.hyps]
+
     def extract_feat(self, samples, sample_rate):
         """extract feat
 
@@ -304,9 +450,10 @@ class ASRServerExecutor(ASRExecutor):
             x_chunk (numpy.array): shape[B, T, D]
             x_chunk_lens (numpy.array): shape[B]
         """
+
+        if "deepspeech2online" in self.model_type:
             # pcm16 -> pcm 32
             samples = pcm2float(samples)
-        if "deepspeech2online" in self.model_type:
             # read audio
             speech_segment = SpeechSegment.from_pcm(
                 samples, sample_rate, transcript=" ")

paddlespeech/server/ws/asr_socket.py

@@ -14,7 +14,6 @@
 import json
 
 import numpy as np
-import json
 from fastapi import APIRouter
 from fastapi import WebSocket
 from fastapi import WebSocketDisconnect
@@ -86,16 +85,21 @@ async def websocket_endpoint(websocket: WebSocket):
                 engine_pool = get_engine_pool()
                 asr_engine = engine_pool['asr']
                 asr_results = ""
-                frames = chunk_buffer.frame_generator(message)
+                # frames = chunk_buffer.frame_generator(message)
-                for frame in frames:
+                # for frame in frames:
-                    # get the pcm data from the bytes
+                #     # get the pcm data from the bytes
-                    samples = np.frombuffer(frame.bytes, dtype=np.int16)
+                #     samples = np.frombuffer(frame.bytes, dtype=np.int16)
+                #     sample_rate = asr_engine.config.sample_rate
+                #     x_chunk, x_chunk_lens = asr_engine.preprocess(samples,
+                #                                                   sample_rate)
+                #     asr_engine.run(x_chunk, x_chunk_lens)
+                #     asr_results = asr_engine.postprocess()
+                samples = np.frombuffer(message, dtype=np.int16)
                 sample_rate = asr_engine.config.sample_rate
                 x_chunk, x_chunk_lens = asr_engine.preprocess(samples,
                                                               sample_rate)
                 asr_engine.run(x_chunk, x_chunk_lens)
-                    asr_results = asr_engine.postprocess()
+                # asr_results = asr_engine.postprocess()
-
                 asr_results = asr_engine.postprocess()
                 resp = {'asr_results': asr_results}