logfile reviewed, whisper_timestamped loading module and vad

 PR #10, issues #9, #30

whisper_online.py

@@ -26,12 +26,15 @@ class ASRBase:
     sep = " "   # join transcribe words with this character (" " for whisper_timestamped,
                 # "" for faster-whisper because it emits the spaces when neeeded)
 
-    def __init__(self, lan, modelsize=None, cache_dir=None, model_dir=None):
+    def __init__(self, lan, modelsize=None, cache_dir=None, model_dir=None, logfile=sys.stderr):
+        self.logfile = logfile
+
         self.transcribe_kargs = {}
         self.original_language = lan 
 
         self.model = self.load_model(modelsize, cache_dir, model_dir)
 
+
     def load_model(self, modelsize, cache_dir):
         raise NotImplemented("must be implemented in the child class")
 
@@ -50,15 +53,18 @@ class WhisperTimestampedASR(ASRBase):
     sep = " "
 
     def load_model(self, modelsize=None, cache_dir=None, model_dir=None):
-        global whisper_timestamped  # has to be global as it is used at each `transcribe` call
         import whisper
-        import whisper_timestamped
+        from whisper_timestamped import transcribe_timestamped
+        self.transcribe_timestamped = transcribe_timestamped
         if model_dir is not None:
             print("ignoring model_dir, not implemented",file=self.logfile)
         return whisper.load_model(modelsize, download_root=cache_dir)
 
     def transcribe(self, audio, init_prompt=""):
-        result = whisper_timestamped.transcribe_timestamped(self.model, audio, language=self.original_language, initial_prompt=init_prompt, verbose=None, condition_on_previous_text=True)
+        result = self.transcribe_timestamped(self.model,
+                audio, language=self.original_language,
+                initial_prompt=init_prompt, verbose=None,
+                condition_on_previous_text=True, **self.transcribe_kargs)
         return result
 
     def ts_words(self,r):
@@ -74,7 +80,12 @@ def segments_end_ts(self, res):
         return [s["end"] for s in res["segments"]]
 
     def use_vad(self):
-        raise NotImplemented("Feature use_vad is not implemented for whisper_timestamped backend.")
+        self.transcribe_kargs["vad"] = True
+
+    def set_translate_task(self):
+        self.transcribe_kargs["task"] = "translate"
+
+
 
 
 class FasterWhisperASR(ASRBase):
@@ -135,7 +146,6 @@ def set_translate_task(self):
 class HypothesisBuffer:
 
     def __init__(self, logfile=sys.stderr):
-        """output: where to store the log. Leave it unchanged to print to terminal."""
         self.commited_in_buffer = []
         self.buffer = []
         self.new = []
@@ -205,7 +215,7 @@ class OnlineASRProcessor:
     def __init__(self, asr, tokenizer, logfile=sys.stderr):
         """asr: WhisperASR object
         tokenizer: sentence tokenizer object for the target language. Must have a method *split* that behaves like the one of MosesTokenizer.
-        output: where to store the log. Leave it unchanged to print to terminal.
+        logfile: where to store the log. 
         """
         self.asr = asr
         self.tokenizer = tokenizer
@@ -468,21 +478,24 @@ def split(self, sent):
     parser.add_argument('--vad', action="store_true", default=False, help='Use VAD = voice activity detection, with the default parameters.')
     args = parser.parse_args()
 
+    # reset to store stderr to different file stream, e.g. open(os.devnull,"w")
+    logfile = sys.stderr
+
     if args.offline and args.comp_unaware:
-        print("No or one option from --offline and --comp_unaware are available, not both. Exiting.",file=sys.stderr)
+        print("No or one option from --offline and --comp_unaware are available, not both. Exiting.",file=logfile)
         sys.exit(1)
 
     audio_path = args.audio_path
 
     SAMPLING_RATE = 16000
     duration = len(load_audio(audio_path))/SAMPLING_RATE
-    print("Audio duration is: %2.2f seconds" % duration, file=sys.stderr)
+    print("Audio duration is: %2.2f seconds" % duration, file=logfile)
 
     size = args.model
     language = args.lan
 
     t = time.time()
-    print(f"Loading Whisper {size} model for {language}...",file=sys.stderr,end=" ",flush=True)
+    print(f"Loading Whisper {size} model for {language}...",file=logfile,end=" ",flush=True)
 
     if args.backend == "faster-whisper":
         asr_cls = FasterWhisperASR
@@ -499,15 +512,15 @@ def split(self, sent):
 
 
     e = time.time()
-    print(f"done. It took {round(e-t,2)} seconds.",file=sys.stderr)
+    print(f"done. It took {round(e-t,2)} seconds.",file=logfile)
 
     if args.vad:
-        print("setting VAD filter",file=sys.stderr)
+        print("setting VAD filter",file=logfile)
         asr.use_vad()
 
 
     min_chunk = args.min_chunk_size
-    online = OnlineASRProcessor(asr,create_tokenizer(tgt_language))
+    online = OnlineASRProcessor(asr,create_tokenizer(tgt_language),logfile=logfile)
 
 
     # load the audio into the LRU cache before we start the timer
@@ -529,18 +542,18 @@ def output_transcript(o, now=None):
         if now is None:
             now = time.time()-start
         if o[0] is not None:
-            print("%1.4f %1.0f %1.0f %s" % (now*1000, o[0]*1000,o[1]*1000,o[2]),file=sys.stderr,flush=True)
+            print("%1.4f %1.0f %1.0f %s" % (now*1000, o[0]*1000,o[1]*1000,o[2]),file=logfile,flush=True)
             print("%1.4f %1.0f %1.0f %s" % (now*1000, o[0]*1000,o[1]*1000,o[2]),flush=True)
         else:
-            print(o,file=sys.stderr,flush=True)
+            print(o,file=logfile,flush=True)
 
     if args.offline: ## offline mode processing (for testing/debugging)
         a = load_audio(audio_path)
         online.insert_audio_chunk(a)
         try:
             o = online.process_iter()
         except AssertionError:
-            print("assertion error",file=sys.stderr)
+            print("assertion error",file=logfile)
             pass
         else:
             output_transcript(o)
@@ -553,12 +566,12 @@ def output_transcript(o, now=None):
             try:
                 o = online.process_iter()
             except AssertionError:
-                print("assertion error",file=sys.stderr)
+                print("assertion error",file=logfile)
                 pass
             else:
                 output_transcript(o, now=end)
 
-            print(f"## last processed {end:.2f}s",file=sys.stderr,flush=True)
+            print(f"## last processed {end:.2f}s",file=logfile,flush=True)
 
             beg = end
             end += min_chunk
@@ -580,12 +593,12 @@ def output_transcript(o, now=None):
             try:
                 o = online.process_iter()
             except AssertionError:
-                print("assertion error",file=sys.stderr)
+                print("assertion error",file=logfile)
                 pass
             else:
                 output_transcript(o)
             now = time.time() - start
-            print(f"## last processed {end:.2f} s, now is {now:.2f}, the latency is {now-end:.2f}",file=sys.stderr,flush=True)
+            print(f"## last processed {end:.2f} s, now is {now:.2f}, the latency is {now-end:.2f}",file=logfile,flush=True)
 
             if end >= duration:
                 break