mirror of
https://github.com/SYSTRAN/faster-whisper.git
synced 2026-01-10 14:07:59 -05:00
bcd8ce0fc7
* Added test for `multilingual` option with english-german audio * removed `output_language` argument as it is redundant, you can get the same functionality with `task="translate"` * use the correct `encoder_output` for language detection in sequential transcription * enabled `multilingual` functionality for batched inference
322 lines
9.1 KiB
Python
322 lines
9.1 KiB
Python
import os
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import numpy as np
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from faster_whisper import BatchedInferencePipeline, WhisperModel, decode_audio
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from faster_whisper.tokenizer import Tokenizer
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from faster_whisper.transcribe import get_suppressed_tokens
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def test_supported_languages():
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model = WhisperModel("tiny.en")
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assert model.supported_languages == ["en"]
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def test_transcribe(jfk_path):
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model = WhisperModel("tiny")
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segments, info = model.transcribe(jfk_path, word_timestamps=True)
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assert info.all_language_probs is not None
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assert info.language == "en"
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assert info.language_probability > 0.9
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assert info.duration == 11
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# Get top language info from all results, which should match the
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# already existing metadata
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top_lang, top_lang_score = info.all_language_probs[0]
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assert info.language == top_lang
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assert abs(info.language_probability - top_lang_score) < 1e-16
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segments = list(segments)
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assert len(segments) == 1
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segment = segments[0]
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assert segment.text == (
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" And so my fellow Americans, ask not what your country can do for you, "
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"ask what you can do for your country."
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)
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assert segment.text == "".join(word.word for word in segment.words)
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assert segment.start == segment.words[0].start
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assert segment.end == segment.words[-1].end
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batched_model = BatchedInferencePipeline(model=model)
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result, info = batched_model.transcribe(
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jfk_path, word_timestamps=True, vad_filter=False
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)
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assert info.language == "en"
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assert info.language_probability > 0.7
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segments = []
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for segment in result:
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segments.append(
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{"start": segment.start, "end": segment.end, "text": segment.text}
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)
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assert len(segments) == 1
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assert segment.text == (
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" And so my fellow Americans ask not what your country can do for you, "
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"ask what you can do for your country."
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)
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def test_batched_transcribe(physcisworks_path):
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model = WhisperModel("tiny")
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batched_model = BatchedInferencePipeline(model=model)
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result, info = batched_model.transcribe(physcisworks_path, batch_size=16)
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assert info.language == "en"
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assert info.language_probability > 0.7
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segments = []
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for segment in result:
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segments.append(
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{"start": segment.start, "end": segment.end, "text": segment.text}
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)
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# number of near 30 sec segments
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assert len(segments) == 7
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result, info = batched_model.transcribe(
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physcisworks_path,
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batch_size=16,
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without_timestamps=False,
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word_timestamps=True,
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)
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segments = []
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for segment in result:
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assert segment.words is not None
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segments.append(
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{"start": segment.start, "end": segment.end, "text": segment.text}
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)
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assert len(segments) > 7
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def test_empty_audio():
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audio = np.asarray([], dtype="float32")
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model = WhisperModel("tiny")
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pipeline = BatchedInferencePipeline(model=model)
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assert list(model.transcribe(audio)[0]) == []
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assert list(pipeline.transcribe(audio)[0]) == []
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model.detect_language(audio)
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def test_prefix_with_timestamps(jfk_path):
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model = WhisperModel("tiny")
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segments, _ = model.transcribe(jfk_path, prefix="And so my fellow Americans")
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segments = list(segments)
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assert len(segments) == 1
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segment = segments[0]
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assert segment.text == (
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" And so my fellow Americans, ask not what your country can do for you, "
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"ask what you can do for your country."
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)
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assert segment.start == 0
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assert 10 < segment.end <= 11
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def test_vad(jfk_path):
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model = WhisperModel("tiny")
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segments, info = model.transcribe(
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jfk_path,
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vad_filter=True,
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vad_parameters=dict(min_silence_duration_ms=500, speech_pad_ms=200),
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)
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segments = list(segments)
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assert len(segments) == 1
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segment = segments[0]
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assert segment.text == (
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" And so my fellow Americans ask not what your country can do for you, "
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"ask what you can do for your country."
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)
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assert 0 < segment.start < 1
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assert 10 < segment.end < 11
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assert info.vad_options.min_silence_duration_ms == 500
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assert info.vad_options.speech_pad_ms == 200
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def test_stereo_diarization(data_dir):
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model = WhisperModel("tiny")
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audio_path = os.path.join(data_dir, "stereo_diarization.wav")
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left, right = decode_audio(audio_path, split_stereo=True)
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segments, _ = model.transcribe(left)
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transcription = "".join(segment.text for segment in segments).strip()
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assert transcription == (
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"He began a confused complaint against the wizard, "
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"who had vanished behind the curtain on the left."
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)
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segments, _ = model.transcribe(right)
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transcription = "".join(segment.text for segment in segments).strip()
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assert transcription == "The horizon seems extremely distant."
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def test_multilingual_transcription(data_dir):
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model = WhisperModel("tiny")
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pipeline = BatchedInferencePipeline(model)
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audio_path = os.path.join(data_dir, "multilingual.mp3")
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audio = decode_audio(audio_path)
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segments, info = model.transcribe(
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audio,
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multilingual=True,
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without_timestamps=True,
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condition_on_previous_text=False,
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)
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segments = list(segments)
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assert (
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segments[0].text
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== " Permission is hereby granted, free of charge, to any person obtaining a copy of the"
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" software and associated documentation files to deal in the software without restriction,"
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" including without limitation the rights to use, copy, modify, merge, publish, distribute"
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", sublicence, and or cell copies of the software, and to permit persons to whom the "
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"software is furnished to do so, subject to the following conditions. The above copyright"
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" notice and this permission notice, shall be included in all copies or substantial "
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"portions of the software."
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)
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assert (
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segments[1].text
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== " Jedem, der dieses Software und die dazu gehöregen Dokumentationsdatein erhält, wird "
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"hiermit unengeltlich die Genehmigung erteilt, wird der Software und eingeschränkt zu "
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"verfahren. Dies umfasst insbesondere das Recht, die Software zu verwenden, zu "
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"vervielfältigen, zu modifizieren, zu Samenzofügen, zu veröffentlichen, zu verteilen, "
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"unterzulizenzieren und oder kopieren der Software zu verkaufen und diese Rechte "
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"unterfolgen den Bedingungen anderen zu übertragen."
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)
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segments, info = pipeline.transcribe(audio, multilingual=True)
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segments = list(segments)
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assert (
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segments[0].text
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== " Permission is hereby granted, free of charge, to any person obtaining a copy of the"
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" software and associated documentation files to deal in the software without restriction,"
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" including without limitation the rights to use, copy, modify, merge, publish, distribute"
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", sublicence, and or cell copies of the software, and to permit persons to whom the "
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"software is furnished to do so, subject to the following conditions. The above copyright"
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" notice and this permission notice, shall be included in all copies or substantial "
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"portions of the software."
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)
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assert (
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"Dokumentationsdatein erhält, wird hiermit unengeltlich die Genehmigung erteilt,"
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" wird der Software und eingeschränkt zu verfahren. Dies umfasst insbesondere das Recht,"
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" die Software zu verwenden, zu vervielfältigen, zu modifizieren"
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in segments[1].text
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)
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def test_suppressed_tokens_minus_1():
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model = WhisperModel("tiny.en")
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tokenizer = Tokenizer(model.hf_tokenizer, False)
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tokens = get_suppressed_tokens(tokenizer, [-1])
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assert tokens == (
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1,
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2,
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7,
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8,
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9,
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10,
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14,
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25,
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26,
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27,
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28,
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29,
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31,
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58,
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59,
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60,
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61,
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62,
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63,
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90,
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91,
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92,
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93,
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357,
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366,
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438,
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532,
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685,
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705,
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796,
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930,
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1058,
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1220,
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1267,
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1279,
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1303,
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1343,
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1377,
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1391,
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1635,
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1782,
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1875,
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2162,
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2361,
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2488,
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3467,
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4008,
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4211,
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4600,
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4808,
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5299,
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5855,
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6329,
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7203,
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9609,
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9959,
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10563,
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10786,
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11420,
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11709,
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11907,
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13163,
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13697,
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13700,
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14808,
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15306,
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16410,
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16791,
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17992,
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19203,
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19510,
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20724,
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22305,
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22935,
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27007,
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30109,
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30420,
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33409,
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34949,
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40283,
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40493,
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40549,
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47282,
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49146,
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50257,
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50357,
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50358,
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50359,
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50360,
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)
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def test_suppressed_tokens_minus_value():
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model = WhisperModel("tiny.en")
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tokenizer = Tokenizer(model.hf_tokenizer, False)
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tokens = get_suppressed_tokens(tokenizer, [13])
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assert tokens == (13, 50257, 50357, 50358, 50359, 50360)
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