From 2c2fb5e1eb1185a081e3507c2aa5c3aafaa2bb6d Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 22 四月 2025 09:56:44 +0800
Subject: [PATCH] Update README.md (#2487)

---
 funasr/models/sense_voice/model.py |   35 ++++++++++++++++++++++++++---------
 1 files changed, 26 insertions(+), 9 deletions(-)

diff --git a/funasr/models/sense_voice/model.py b/funasr/models/sense_voice/model.py
index 70cd02e..c5d0e59 100644
--- a/funasr/models/sense_voice/model.py
+++ b/funasr/models/sense_voice/model.py
@@ -919,17 +919,28 @@
 
                 timestamp = []
                 tokens = tokenizer.text2tokens(text)[4:]
+                token_back_to_id = tokenizer.tokens2ids(tokens)
+                token_ids = []
+                for tok_ls in token_back_to_id:
+                    if tok_ls: token_ids.extend(tok_ls)
+                    else: token_ids.append(124)
+
+                if len(token_ids) == 0:
+                    result_i = {"key": key[i], "text": text}
+                    results.append(result_i)
+                    continue
+
                 logits_speech = self.ctc.softmax(encoder_out)[i, 4 : encoder_out_lens[i].item(), :]
                 pred = logits_speech.argmax(-1).cpu()
                 logits_speech[pred == self.blank_id, self.blank_id] = 0
                 align = ctc_forced_align(
                     logits_speech.unsqueeze(0).float(),
-                    torch.Tensor(token_int[4:]).unsqueeze(0).long().to(logits_speech.device),
-                    (encoder_out_lens - 4).long(),
-                    torch.tensor(len(token_int) - 4).unsqueeze(0).long().to(logits_speech.device),
+                    torch.Tensor(token_ids).unsqueeze(0).long().to(logits_speech.device),
+                    (encoder_out_lens[i] - 4).long(),
+                    torch.tensor(len(token_ids)).unsqueeze(0).long().to(logits_speech.device),
                     ignore_id=self.ignore_id,
                 )
-                pred = groupby(align[0, : encoder_out_lens[0]])
+                pred = groupby(align[0, : encoder_out_lens[i]])
                 _start = 0
                 token_id = 0
                 ts_max = encoder_out_lens[i] - 4
@@ -951,20 +962,26 @@
 
     def post(self, timestamp):
         timestamp_new = []
+        prev_word = None
         for i, t in enumerate(timestamp):
             word, start, end = t
+            start = int(start * 1000)
+            end = int(end * 1000)
             if word == "鈻�":
                 continue
             if i == 0:
                 # timestamp_new.append([word, start, end])
-                timestamp_new.append([int(start * 1000), int(end * 1000)])
-            elif word.startswith("鈻�") or len(word) == 1 or not word[1].isalpha():
+                timestamp_new.append([start, end])
+            elif word.startswith("鈻�"):
                 word = word[1:]
-                # timestamp_new.append([word, start, end])
-                timestamp_new.append([int(start * 1000), int(end * 1000)])
+                timestamp_new.append([start, end])
+            elif prev_word is not None and prev_word.isalpha() and prev_word.isascii() and word.isalpha() and word.isascii():
+                prev_word += word
+                timestamp_new[-1][1] = end
             else:
                 # timestamp_new[-1][0] += word
-                timestamp_new[-1][1] = int(end * 1000)
+                timestamp_new.append([start, end])
+            prev_word = word
         return timestamp_new
 
     def export(self, **kwargs):

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