remove space if existing

fix

egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py

@@ -382,9 +382,7 @@ def decode_dataset(
         for lm_scale, hyps in hyps_dict.items():
             this_batch = []
             assert len(hyps) == len(texts)
-            for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
-                print(f"ref: {''.join(ref_text)}")
-                print(f"hyp: {''.join(hyp_words)}")
+            for cut_id, hyp_text, ref_text in zip(cut_ids, hyps, texts):
                 this_batch.append((cut_id, ref_text, hyp_words))
 
             results[lm_scale].extend(this_batch)
@@ -401,40 +399,38 @@ def decode_dataset(
 def save_results(
     params: AttributeDict,
     test_set_name: str,
-    results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
+    results_dict: Dict[str, List[Tuple[List[int], List[int]]]],
 ):
-
-    enable_log = True
     test_set_wers = dict()
     for key, results in results_dict.items():
         recog_path = (
             params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
         )
         results = sorted(results)
-        store_transcripts(filename=recog_path, texts=results)
-        if enable_log:
-            logging.info(f"The transcripts are stored in {recog_path}")
+        store_transcripts(filename=recog_path, texts=results, char_level=True)
+        logging.info(f"The transcripts are stored in {recog_path}")
 
-        # The following prints out WERs, per-word error statistics and aligned
+        # The following prints out CERs, per-word error statistics and aligned
         # ref/hyp pairs.
         errs_filename = (
             params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
         )
-        # we compute CER for aishell dataset.
-        results_char = []
-        for res in results:
-            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
-                f, f"{test_set_name}-{key}", results_char, enable_log=enable_log
+                f,
+                f"{test_set_name}-{key}",
+                results,
+                enable_log=True,
+                compute_CER=True,
             )
             test_set_wers[key] = wer
 
-        if enable_log:
-            logging.info("Wrote detailed error stats to {}".format(errs_filename))
+        logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = params.res_dir / f"cer-summary-{test_set_name}-{params.suffix}.txt"
+    errs_info = (
+        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+    )
     with open(errs_info, "w") as f:
         print("settings\tCER", file=f)
         for key, val in test_set_wers:

egs/speech_llm/ASR_LLM/whisper_llm_zh/train.py

@@ -339,6 +339,7 @@ def compute_loss(
 
     messages = []
     for i, text in enumerate(texts):
+        text = text.replace(" ", "")
         message = [
             {"role": "user", "content": f"{DEFAULT_SPEECH_TOKEN}请转写音频为文字"},
             {"role": "assistant", "content": text},