Add new tests (#1158)

tests/test_tokenizer.py

@@ -0,0 +1,120 @@
+from faster_whisper import WhisperModel
+from faster_whisper.tokenizer import Tokenizer
+from faster_whisper.transcribe import get_suppressed_tokens
+
+
+def test_suppressed_tokens_minus_1():
+    model = WhisperModel("tiny.en")
+
+    tokenizer = Tokenizer(model.hf_tokenizer, False)
+    tokens = get_suppressed_tokens(tokenizer, [-1])
+    assert tokens == (
+        1,
+        2,
+        7,
+        8,
+        9,
+        10,
+        14,
+        25,
+        26,
+        27,
+        28,
+        29,
+        31,
+        58,
+        59,
+        60,
+        61,
+        62,
+        63,
+        90,
+        91,
+        92,
+        93,
+        357,
+        366,
+        438,
+        532,
+        685,
+        705,
+        796,
+        930,
+        1058,
+        1220,
+        1267,
+        1279,
+        1303,
+        1343,
+        1377,
+        1391,
+        1635,
+        1782,
+        1875,
+        2162,
+        2361,
+        2488,
+        3467,
+        4008,
+        4211,
+        4600,
+        4808,
+        5299,
+        5855,
+        6329,
+        7203,
+        9609,
+        9959,
+        10563,
+        10786,
+        11420,
+        11709,
+        11907,
+        13163,
+        13697,
+        13700,
+        14808,
+        15306,
+        16410,
+        16791,
+        17992,
+        19203,
+        19510,
+        20724,
+        22305,
+        22935,
+        27007,
+        30109,
+        30420,
+        33409,
+        34949,
+        40283,
+        40493,
+        40549,
+        47282,
+        49146,
+        50257,
+        50357,
+        50358,
+        50359,
+        50360,
+    )
+
+
+def test_suppressed_tokens_minus_value():
+    model = WhisperModel("tiny.en")
+
+    tokenizer = Tokenizer(model.hf_tokenizer, False)
+    tokens = get_suppressed_tokens(tokenizer, [13])
+    assert tokens == (13, 50257, 50357, 50358, 50359, 50360)
+
+
+def test_split_on_unicode():
+    model = WhisperModel("tiny")
+    tokenizer = Tokenizer(model.hf_tokenizer, False)
+
+    tokens = [8404, 871, 287, 6, 246, 526, 3210, 20378]
+    words, word_tokens = tokenizer.split_tokens_on_unicode(tokens)
+
+    assert words == [" elle", " est", " l", "'", "\ufffd", "é", "rit", "oire"]
+    assert word_tokens == [[8404], [871], [287], [6], [246], [526], [3210], [20378]]

tests/test_transcribe.py

@@ -1,10 +1,9 @@
+import inspect
 import os
 
 import numpy as np
 
 from faster_whisper import BatchedInferencePipeline, WhisperModel, decode_audio
-from faster_whisper.tokenizer import Tokenizer
-from faster_whisper.transcribe import get_suppressed_tokens
 
 
 def test_supported_languages():
@@ -215,107 +214,58 @@ def test_multilingual_transcription(data_dir):
     )
 
 
-def test_suppressed_tokens_minus_1():
-    model = WhisperModel("tiny.en")
+def test_hotwords(data_dir):
+    model = WhisperModel("tiny")
+    pipeline = BatchedInferencePipeline(model)
 
-    tokenizer = Tokenizer(model.hf_tokenizer, False)
-    tokens = get_suppressed_tokens(tokenizer, [-1])
-    assert tokens == (
-        1,
-        2,
-        7,
-        8,
-        9,
-        10,
-        14,
-        25,
-        26,
-        27,
-        28,
-        29,
-        31,
-        58,
-        59,
-        60,
-        61,
-        62,
-        63,
-        90,
-        91,
-        92,
-        93,
-        357,
-        366,
-        438,
-        532,
-        685,
-        705,
-        796,
-        930,
-        1058,
-        1220,
-        1267,
-        1279,
-        1303,
-        1343,
-        1377,
-        1391,
-        1635,
-        1782,
-        1875,
-        2162,
-        2361,
-        2488,
-        3467,
-        4008,
-        4211,
-        4600,
-        4808,
-        5299,
-        5855,
-        6329,
-        7203,
-        9609,
-        9959,
-        10563,
-        10786,
-        11420,
-        11709,
-        11907,
-        13163,
-        13697,
-        13700,
-        14808,
-        15306,
-        16410,
-        16791,
-        17992,
-        19203,
-        19510,
-        20724,
-        22305,
-        22935,
-        27007,
-        30109,
-        30420,
-        33409,
-        34949,
-        40283,
-        40493,
-        40549,
-        47282,
-        49146,
-        50257,
-        50357,
-        50358,
-        50359,
-        50360,
+    audio_path = os.path.join(data_dir, "hotwords.mp3")
+    audio = decode_audio(audio_path)
+
+    segments, info = model.transcribe(audio, hotwords="ComfyUI")
+    segments = list(segments)
+
+    assert "ComfyUI" in segments[0].text
+    assert info.transcription_options.hotwords == "ComfyUI"
+
+    segments, info = pipeline.transcribe(audio, hotwords="ComfyUI")
+    segments = list(segments)
+
+    assert "ComfyUI" in segments[0].text
+    assert info.transcription_options.hotwords == "ComfyUI"
+
+
+def test_transcribe_signature():
+    model_transcribe_args = set(inspect.getargs(WhisperModel.transcribe.__code__).args)
+    pipeline_transcribe_args = set(
+        inspect.getargs(BatchedInferencePipeline.transcribe.__code__).args
     )
+    pipeline_transcribe_args.remove("batch_size")
+
+    assert model_transcribe_args == pipeline_transcribe_args
 
 
-def test_suppressed_tokens_minus_value():
-    model = WhisperModel("tiny.en")
+def test_monotonic_timestamps(physcisworks_path):
+    model = WhisperModel("tiny")
+    pipeline = BatchedInferencePipeline(model=model)
 
-    tokenizer = Tokenizer(model.hf_tokenizer, False)
-    tokens = get_suppressed_tokens(tokenizer, [13])
-    assert tokens == (13, 50257, 50357, 50358, 50359, 50360)
+    segments, info = model.transcribe(physcisworks_path, word_timestamps=True)
+    segments = list(segments)
+
+    for i in range(len(segments) - 1):
+        assert segments[i].start <= segments[i].end
+        assert segments[i].end <= segments[i + 1].start
+        for word in segments[i].words:
+            assert word.start <= word.end
+            assert word.end <= segments[i].end
+    assert segments[-1].end <= info.duration
+
+    segments, info = pipeline.transcribe(physcisworks_path, word_timestamps=True)
+    segments = list(segments)
+
+    for i in range(len(segments) - 1):
+        assert segments[i].start <= segments[i].end
+        assert segments[i].end <= segments[i + 1].start
+        for word in segments[i].words:
+            assert word.start <= word.end
+            assert word.end <= segments[i].end
+    assert segments[-1].end <= info.duration