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import os
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():
model = WhisperModel("tiny.en")
assert model.supported_languages == ["en"]
def test_transcribe(jfk_path):
model = WhisperModel("tiny")
segments, info = model.transcribe(jfk_path, word_timestamps=True)
assert info.all_language_probs is not None
assert info.language == "en"
assert info.language_probability > 0.9
assert info.duration == 11
# Get top language info from all results, which should match the
# already existing metadata
top_lang, top_lang_score = info.all_language_probs[0]
assert info.language == top_lang
assert abs(info.language_probability - top_lang_score) < 1e-16
segments = list(segments)
assert len(segments) == 1
segment = segments[0]
assert segment.text == (
" And so my fellow Americans ask not what your country can do for you, "
"ask what you can do for your country."
)
assert segment.text == "".join(word.word for word in segment.words)
assert segment.start == segment.words[0].start
assert segment.end == segment.words[-1].end
batched_model = BatchedInferencePipeline(model=model, use_vad_model=False)
result, info = batched_model.transcribe(jfk_path, word_timestamps=True)
assert info.language == "en"
assert info.language_probability > 0.7
segments = []
for segment in result:
segments.append(
{"start": segment.start, "end": segment.end, "text": segment.text}
)
assert len(segments) == 1
assert segment.text == (
" And so my fellow Americans ask not what your country can do for you, "
"ask what you can do for your country."
)
def test_batched_transcribe(physcisworks_path):
model = WhisperModel("tiny")
batched_model = BatchedInferencePipeline(model=model)
result, info = batched_model.transcribe(physcisworks_path, batch_size=16)
assert info.language == "en"
assert info.language_probability > 0.7
segments = []
for segment in result:
segments.append(
{"start": segment.start, "end": segment.end, "text": segment.text}
)
# number of near 30 sec segments
assert len(segments) == 8
result, info = batched_model.transcribe(
physcisworks_path,
batch_size=16,
without_timestamps=False,
word_timestamps=True,
)
segments = []
for segment in result:
assert segment.words is not None
segments.append(
{"start": segment.start, "end": segment.end, "text": segment.text}
)
assert len(segments) > 8
def test_prefix_with_timestamps(jfk_path):
model = WhisperModel("tiny")
segments, _ = model.transcribe(jfk_path, prefix="And so my fellow Americans")
segments = list(segments)
assert len(segments) == 1
segment = segments[0]
assert segment.text == (
" And so my fellow Americans ask not what your country can do for you, "
"ask what you can do for your country."
)
assert segment.start == 0
assert 10 < segment.end < 11
def test_vad(jfk_path):
model = WhisperModel("tiny")
segments, info = model.transcribe(
jfk_path,
vad_filter=True,
vad_parameters=dict(min_silence_duration_ms=500, speech_pad_ms=200),
)
segments = list(segments)
assert len(segments) == 1
segment = segments[0]
assert segment.text == (
" And so my fellow Americans ask not what your country can do for you, "
"ask what you can do for your country."
)
assert 0 < segment.start < 1
assert 10 < segment.end < 11
assert info.vad_options.min_silence_duration_ms == 500
assert info.vad_options.speech_pad_ms == 200
def test_stereo_diarization(data_dir):
model = WhisperModel("tiny")
audio_path = os.path.join(data_dir, "stereo_diarization.wav")
left, right = decode_audio(audio_path, split_stereo=True)
segments, _ = model.transcribe(left)
transcription = "".join(segment.text for segment in segments).strip()
assert transcription == (
"He began a confused complaint against the wizard, "
"who had vanished behind the curtain on the left."
)
segments, _ = model.transcribe(right)
transcription = "".join(segment.text for segment in segments).strip()
assert transcription == "The horizon seems extremely distant."
def test_multisegment_lang_id(physcisworks_path):
model = WhisperModel("tiny")
language_info = model.detect_language_multi_segment(physcisworks_path)
assert language_info["language_code"] == "en"
assert language_info["language_confidence"] > 0.8
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)
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