streaming asr server add time stamp, test=doc

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

@@ -13,7 +13,6 @@
 # limitations under the License.
 import copy
 import os
-import time
 from typing import Optional
 
 import numpy as np
@@ -298,6 +297,7 @@ class PaddleASRConnectionHanddler:
         self.global_frame_offset = 0
         self.result_transcripts = ['']
         self.first_char_occur_elapsed = None
+        self.word_time_stamp = None
 
     def decode(self, is_finished=False):
         if "deepspeech2online" in self.model_type:
@@ -513,6 +513,12 @@ class PaddleASRConnectionHanddler:
         else:
             return ''
 
+    def get_word_time_stamp(self):
+        if self.word_time_stamp is None:
+            return []
+        else:
+            return self.word_time_stamp
+
     @paddle.no_grad()
     def rescoring(self):
         if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type:
@@ -577,8 +583,35 @@ class PaddleASRConnectionHanddler:
         # update the one best result
         logger.info(f"best index: {best_index}")
         self.hyps = [hyps[best_index][0]]
+
+        # update the hyps time stamp
+        self.time_stamp = hyps[best_index][5] if hyps[best_index][2] > hyps[
+            best_index][3] else hyps[best_index][6]
+        logger.info(f"time stamp: {self.time_stamp}")
+
         self.update_result()
 
+        # update each word start and end time stamp
+        frame_shift_in_ms = self.model.encoder.embed.subsampling_rate * self.n_shift / self.sample_rate
+        logger.info(f"frame shift ms: {frame_shift_in_ms}")
+        word_time_stamp = []
+        for idx, _ in enumerate(self.time_stamp):
+            start = (self.time_stamp[idx - 1] + self.time_stamp[idx]
+                     ) / 2.0 if idx > 0 else 0
+            start = start * frame_shift_in_ms
+
+            end = (self.time_stamp[idx] + self.time_stamp[idx + 1]
+                   ) / 2.0 if idx < len(self.time_stamp) - 1 else self.offset
+            end = end * frame_shift_in_ms
+            word_time_stamp.append({
+                "w": self.result_transcripts[0][idx],
+                "bg": start,
+                "ed": end
+            })
+            # logger.info(f"{self.result_transcripts[0][idx]}, start: {start}, end: {end}")
+        self.word_time_stamp = word_time_stamp
+        logger.info(f"word time stamp: {self.word_time_stamp}")
+
 
 class ASRServerExecutor(ASRExecutor):
     def __init__(self):

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

@@ -11,6 +11,7 @@
 # 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 copy
 from collections import defaultdict
 
 import paddle
@@ -54,14 +55,24 @@ class CTCPrefixBeamSearch:
         assert len(ctc_probs.shape) == 2
 
         # cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score))
-        # blank_ending_score and  none_blank_ending_score in ln domain
+        # 0. blank_ending_score,
+        # 1. none_blank_ending_score, 
+        # 2. viterbi_blank ending, 
+        # 3. viterbi_non_blank, 
+        # 4. current_token_prob, 
+        # 5. times_viterbi_blank, 
+        # 6. times_titerbi_non_blank
         if self.cur_hyps is None:
-            self.cur_hyps = [(tuple(), (0.0, -float('inf')))]
+            self.cur_hyps = [(tuple(), (0.0, -float('inf'), 0.0, 0.0,
+                                        -float('inf'), [], []))]
+            # 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')))
+            # next_hyps = defaultdict(lambda: (-float('inf'), -float('inf')))
+            next_hyps = defaultdict(
+                        lambda: (-float('inf'), -float('inf'), -float('inf'), -float('inf'), -float('inf'), [], []))
 
             # 2.1 First beam prune: select topk best
             #     do token passing process
@@ -69,36 +80,83 @@ class CTCPrefixBeamSearch:
             for s in top_k_index:
                 s = s.item()
                 ps = logp[s].item()
-                for prefix, (pb, pnb) in self.cur_hyps:
+                for prefix, (pb, pnb, v_s, v_ns, cur_token_prob, times_s,
+                             times_ns) 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, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
+                            prefix]
                         n_pb = log_add([n_pb, pb + ps, pnb + ps])
-                        next_hyps[prefix] = (n_pb, n_pnb)
+
+                        pre_times = times_s if v_s > v_ns else times_ns
+                        n_times_s = copy.deepcopy(pre_times)
+                        viterbi_score = v_s if v_s > v_ns else v_ns
+                        n_v_s = viterbi_score + ps
+                        next_hyps[prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
+                                             n_cur_token_prob, n_times_s,
+                                             n_times_ns)
                     elif s == last:
                         #  Update *ss -> *s;
-                        n_pb, n_pnb = next_hyps[prefix]
+                        # case1: *a + a => *a
+                        n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
+                            prefix]
                         n_pnb = log_add([n_pnb, pnb + ps])
-                        next_hyps[prefix] = (n_pb, n_pnb)
+                        if n_v_ns < v_ns + ps:
+                            n_v_ns = v_ns + ps
+                            if n_cur_token_prob < ps:
+                                n_cur_token_prob = ps
+                                n_times_ns = copy.deepcopy(times_ns)
+                                n_times_ns[
+                                    -1] = self.abs_time_step  # 注意，这里要重新使用绝对时间
+                        next_hyps[prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
+                                             n_cur_token_prob, n_times_s,
+                                             n_times_ns)
+
                         # Update *s-s -> *ss, - is for blank
+                        # Case 2: *aε + a => *aa
                         n_prefix = prefix + (s, )
-                        n_pb, n_pnb = next_hyps[n_prefix]
+                        n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
+                            n_prefix]
+                        if n_v_ns < v_s + ps:
+                            n_v_ns = v_s + ps
+                            n_cur_token_prob = ps
+                            n_times_ns = copy.deepcopy(times_s)
+                            n_times_ns.append(self.abs_time_step)
                         n_pnb = log_add([n_pnb, pb + ps])
-                        next_hyps[n_prefix] = (n_pb, n_pnb)
+                        next_hyps[n_prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
+                                               n_cur_token_prob, n_times_s,
+                                               n_times_ns)
                     else:
+                        # Case 3: *a + b => *ab, *aε + b => *ab
                         n_prefix = prefix + (s, )
-                        n_pb, n_pnb = next_hyps[n_prefix]
+                        n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_n = next_hyps[
+                            n_prefix]
+                        viterbi_score = v_s if v_s > v_ns else v_ns
+                        pre_times = times_s if v_s > v_ns else times_ns
+                        if n_v_ns < viterbi_score + ps:
+                            n_v_ns = viterbi_score + ps
+                            n_cur_token_prob = ps
+                            n_times_ns = copy.deepcopy(pre_times)
+                            n_times_ns.append(self.abs_time_step)
+
                         n_pnb = log_add([n_pnb, pb + ps, pnb + ps])
-                        next_hyps[n_prefix] = (n_pb, n_pnb)
+                        next_hyps[n_prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
+                                               n_cur_token_prob, n_times_s,
+                                               n_times_ns)
 
             # 2.2 Second beam prune
             next_hyps = sorted(
                 next_hyps.items(),
-                key=lambda x: log_add(list(x[1])),
+                key=lambda x: log_add([x[1][0], x[1][1]]),
                 reverse=True)
             self.cur_hyps = next_hyps[:beam_size]
 
-        self.hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in self.cur_hyps]
+            # 2.3 update the absolute time step
+            self.abs_time_step += 1
+        # self.hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in self.cur_hyps]
+        self.hyps = [(y[0], log_add([y[1][0], y[1][1]]), y[1][2], y[1][3],
+                      y[1][4], y[1][5], y[1][6]) for y in self.cur_hyps]
+
         logger.info("ctc prefix search success")
         return self.hyps
 
@@ -123,6 +181,7 @@ class CTCPrefixBeamSearch:
         """
         self.cur_hyps = None
         self.hyps = None
+        self.abs_time_step = 0
 
     def finalize_search(self):
         """do nothing in ctc_prefix_beam_search

paddlespeech/server/ws/asr_socket.py

@@ -78,12 +78,14 @@ async def websocket_endpoint(websocket: WebSocket):
                     connection_handler.decode(is_finished=True)
                     connection_handler.rescoring()
                     asr_results = connection_handler.get_result()
+                    word_time_stamp = connection_handler.get_word_time_stamp()
                     connection_handler.reset()
 
                     resp = {
                         "status": "ok",
                         "signal": "finished",
-                        'result': asr_results
+                        'result': asr_results,
+                        'times': word_time_stamp
                     }
                     await websocket.send_json(resp)
                     break