handle negative / tiny duration segments, final

README.md

@@ -29,7 +29,7 @@
 
 <h6 align="center">Made by Max Bain • :globe_with_meridians: <a href="https://www.maxbain.com">https://www.maxbain.com</a></h6>
 
-<img width="1216" align="center" alt="whisperx-arch" src="https://user-images.githubusercontent.com/36994049/208313881-903ab3ea-4932-45fd-b3dc-70876cddaaa2.png">
+<img width="1216" align="center" alt="whisperx-arch" src="https://user-images.githubusercontent.com/36994049/211200186-8b779e26-0bfd-4127-aee2-5a9238b95e1f.png">
 
 
 <p align="left">Whisper-Based Automatic Speech Recognition (ASR) with improved timestamp accuracy using forced alignment.
@@ -64,6 +64,7 @@ $ cd whisperX
 $ pip install -e .
 ```
 
+
 You may also need to install ffmpeg, rust etc. Follow openAI instructions here https://github.com/openai/whisper#setup.
 
 <h2 align="left" id="example">Usage 💬 (command line)</h2>
@@ -101,7 +102,7 @@ Currently default models provided for `{en, fr, de, es, it, ja, zh, nl, uk}`. If
 https://user-images.githubusercontent.com/36994049/208298811-e36002ba-3698-4731-97d4-0aebd07e0eb3.mov
 
 
-See more exac
+See more examples in other languages [here](EXAMPLES.md).
 
 ## Python usage  🐍
 
@@ -127,6 +128,16 @@ print(result_aligned["segments"]) # after alignment
 print(result_aligned["word_segments"]) # after alignment
 ```
 
+
+<h2 align="left" id="whisper-mod">Whisper Modifications</h2>
+
+In addition to forced alignment, the following two modifications have been made to the whisper transcription method:
+
+1. `--condition_on_prev_text` is set to `False` by default (reduces hallucination)
+
+2. Clamping segment `end_time` to be at least 0.02s (one time precision) later than `start_time` (prevents segments with negative duration)
+
+
 <h2 align="left" id="limitations">Limitations ⚠️</h2>
 
 - Not thoroughly tested, especially for non-english, results may vary -- please post issue to let me know the results on your data

whisperx/transcribe.py

@@ -223,6 +223,10 @@ def transcribe(
                     end_timestamp_position = (
                         sliced_tokens[-1].item() - tokenizer.timestamp_begin
                     )
+
+                    # clamp end-time to at least be 1 frame after start-time
+                    end_timestamp_position = max(end_timestamp_position, start_timestamp_position + time_precision)
+
                     add_segment(
                         start=timestamp_offset + start_timestamp_position * time_precision,
                         end=timestamp_offset + end_timestamp_position * time_precision,
@@ -291,28 +295,27 @@ def align(
     prev_t2 = 0
     word_segments_list = []
     for idx, segment in enumerate(transcript):
-        if int(segment['start'] * SAMPLE_RATE) >= audio.shape[1]:
+        # first we pad
-            print("Failed to align segment: original start time longer than audio duration, skipping...")
-            continue
-        
-        if int(segment['start']) >= int(segment['end']):
-            print("Failed to align segment: original end time is not after start time, skipping...")
-            continue
-
         t1 = max(segment['start'] - extend_duration, 0)
         t2 = min(segment['end'] + extend_duration, MAX_DURATION)
+
+        # use prev_t2 as current t1 if it's later
         if start_from_previous and t1 < prev_t2:
             t1 = prev_t2
 
+        # check if timestamp range is still valid
+        if t1 >= MAX_DURATION:
+            print("Failed to align segment: original start time longer than audio duration, skipping...")
+            continue
+        if t2 - t1 < 0.02:
+            print("Failed to align segment: duration smaller than 0.02s time precision")
+            continue
+
         f1 = int(t1 * SAMPLE_RATE)
         f2 = int(t2 * SAMPLE_RATE)
 
-
         waveform_segment = audio[:, f1:f2]
 
-        if waveform_segment.shape[1] < 10:
-            print("Failed to align segment: too short in duration, %.3f" % waveform_segment.shape[1]/SAMPLE_RATE)
-            continue
         with torch.inference_mode():
             if model_type == "torchaudio":
                 emissions, _ = model(waveform_segment.to(device))
@@ -321,6 +324,7 @@ def align(
             else:
                 raise NotImplementedError(f"Align model of type {model_type} not supported.")
             emissions = torch.log_softmax(emissions, dim=-1)
+
         emission = emissions[0].cpu().detach()
         transcription = segment['text'].strip()
         if model_lang not in LANGUAGES_WITHOUT_SPACES:
@@ -519,6 +523,7 @@ def cli():
             print(f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language...")
             align_model, align_metadata = load_align_model(result["language"], device)
 
+        print("Performing alignment...")
         result_aligned = align(result["segments"], align_model, align_metadata, audio_path, device,
                                 extend_duration=align_extend, start_from_previous=align_from_prev, drop_non_aligned_words=drop_non_aligned)
         audio_basename = os.path.basename(audio_path)