From c4883e979e45f4f9696ac82eba9ca72711c28535 Mon Sep 17 00:00:00 2001 From: chadlagore Date: Tue, 29 May 2018 00:04:25 -0700 Subject: [PATCH 1/5] Add prediction output --- minutes/__init__.py | 7 ++++--- minutes/audio.py | 28 ++++++++++++++++++++-------- minutes/base.py | 33 ++++++++++++++++++++++++++++++--- minutes/conversation.py | 21 +++++++++++++++------ minutes/minutes.py | 22 ++++++---------------- minutes/phrase.py | 12 ++++++++++++ test/config.py | 7 ++++++- test/test_conversation.py | 6 ++++++ test/test_minutes.py | 19 +++++++++++++++++++ 9 files changed, 118 insertions(+), 37 deletions(-) create mode 100644 minutes/phrase.py create mode 100644 test/test_conversation.py diff --git a/minutes/__init__.py b/minutes/__init__.py index 0cdeec5..b3baccf 100644 --- a/minutes/__init__.py +++ b/minutes/__init__.py @@ -1,3 +1,4 @@ -from .minutes import Minutes # noqa -from .speaker import Speaker # noqa -from .conversation import Conversation # noqa +from minutes.minutes import Minutes # noqa +from minutes.speaker import Speaker # noqa +from minutes.conversation import Conversation # noqa +from minutes.phrase import Phrase # noqa diff --git a/minutes/audio.py b/minutes/audio.py index 808d974..8f9947b 100644 --- a/minutes/audio.py +++ b/minutes/audio.py @@ -40,18 +40,16 @@ def samples_per_observation(self, ms_per_observation): """ return int(self.rate * ms_per_observation / 1000.) - def get_spectrograms(self, ms_per_observation, verbose=False): - """Converts a internal table of raw audio audio phrases into with - one spectrogram per row. + def get_observations(self, ms_per_observation, verbose=False): + """Converts a internal raw audio vector into table with + one observations per row. Arguments: ms_per_observation {int} -- The number of desired ms per obs. Returns: - np.array -- An array of spectrograms, one per row. The width - of each spectrogram depends on the ms_per_observation, - The number of rows depends on the length of the audio file - and the ms per observations. + np.array -- An array of observations, one per row. The width + of each row depends on the ms_per_observation, """ d = self.samples_per_observation(ms_per_observation) N = len(self.data) // d @@ -70,7 +68,21 @@ def get_spectrograms(self, ms_per_observation, verbose=False): print('Truncating {} bytes from end of sample'.format(t)) # Reshape for processing into spectrograms. - data = data.reshape((N, d)) + return data.reshape((N, d)) + + def get_spectrograms(self, ms_per_observation, verbose=False): + """Converts a internal raw audio vector into table with + one spectrogram per row. + + Arguments: + ms_per_observation {int} -- The number of desired ms per obs. + + Returns: + np.array -- An array of spectrograms, one per row. The width + of each spectrogram depends on the ms_per_observation. + """ + # Reshape for processing into spectrograms. + data = self.get_observations(ms_per_observation, verbose) def spec_from_row(row): _, _, Sxx = signal.spectrogram(row) diff --git a/minutes/base.py b/minutes/base.py index 7893f18..253416f 100644 --- a/minutes/base.py +++ b/minutes/base.py @@ -1,6 +1,5 @@ import json import os -import pickle from keras import backend as K from keras.models import Sequential, load_model @@ -12,6 +11,7 @@ from sklearn.model_selection import train_test_split from minutes.models import MINUTES_BASE_MODEL_DIRECTORY +from minutes.phrase import Phrase class BaseModel: @@ -63,7 +63,7 @@ def load_model(cls, name): def __init__(self, name, ms_per_observation=3000, test_size=0.33, random_state=42): self.name = name - self.speakers = set() + self.speakers = [] self.test_size = test_size self.random_state = random_state self.ms_per_observation = ms_per_observation @@ -78,7 +78,7 @@ def add_speaker(self, speaker): """ if speaker in self.speakers: raise LookupError(f'Speaker {speaker.name} already added.') - self.speakers.add(speaker) + self.speakers.append(speaker) def add_speakers(self, speakers): """Add a collection of speakers to the model. @@ -153,3 +153,30 @@ def save_model(self): # Save internal model. if self.model is not None: self.model.save(os.path.join(self.home, 'keras.h5')) + + def phrases(self, conversation): + """Predict against a new conversation. + + Arguments: + conversation {Conversation} -- A conversation built from an audio + sample. + + Returns: + List[Phrase] -- A list of Phrases. + """ + # Predict against the conversation spectrograms. + X_hat = conversation.get_spectrograms(self.ms_per_observation) + result = self.model.predict(X_hat) + y_hat_indices = np.argmax(result, axis=1) + y_hat = np.array(self.speakers)[y_hat_indices] + + # Convert to a list of phrases. + # TODO: This is extra work, we already converted the + # observations when we called get_spectrograms. We can + # speed things up by somehow caching that result (or returning it) + # if it is prohibitably costly to regenerate these. + obs = conversation.get_observations(self.ms_per_observation) + return [Phrase(o, speaker) for o, speaker in zip(obs, y_hat)] + + def __str__(self): + return self.name diff --git a/minutes/conversation.py b/minutes/conversation.py index 9f18b1d..446fd82 100644 --- a/minutes/conversation.py +++ b/minutes/conversation.py @@ -3,18 +3,28 @@ class Conversation: - def __init__(self, audio_loc, speakers): + def __init__(self, audio_loc): """Create a new conversation from audio sample. Arguments: audio_loc {str} -- The absolute location of an audio conversation sample. - speakers {List[Speaker]} -- A list of speakers included in this - conversation. """ - self.speakers = speakers self.audio = Audio(audio_loc) + def get_spectrograms(self, ms_per_observation, verbose=False): + """Converts the conversation audio sample into an n x d matrix of + spectrograms. + + Keyword Arguments: + verbose {bool} -- (default: {False}) + ms_per_observation {int} -- (default: {False}) + + Returns: + np.array -- An n x d matrix of spectrograms. + """ + return self.audio.get_spectrograms(ms_per_observation, verbose) + def get_observations(self, ms_per_observation, verbose=False): """Converts the conversation audio sample into an n x d matrix of observations. @@ -26,5 +36,4 @@ def get_observations(self, ms_per_observation, verbose=False): Returns: np.array -- An n x d matrix of observations. """ - return self.audio.get_spectrograms(ms_per_observation, verbose) - + return self.audio.get_observations(ms_per_observation, verbose) diff --git a/minutes/minutes.py b/minutes/minutes.py index 2ee20b3..0a99aaf 100644 --- a/minutes/minutes.py +++ b/minutes/minutes.py @@ -31,12 +31,13 @@ def __init__(self, parent='cnn', test_size=0.33, random_state=42): # Load in parent, copy in fixed parameters. self.parent = BaseModel.load_model(parent) - self.ms_per_observation = self.parent.ms_per_observation - self.model = None - self.speakers = set() - self.test_size = test_size - self.random_state = random_state + super().__init__( + self.parent.name + '-child', + self.parent.ms_per_observation, + test_size, + random_state, + ) def fit(self, verbose=0): """Trains the model, given the speakers currently added.""" @@ -72,14 +73,3 @@ def fit(self, verbose=0): batch_size=16, verbose=verbose ) - - def phrases(self, conversation): - """Predict against a new conversation. - - Arguments: - conversation {Conversation} -- A conversation built from an audio - sample. - - Returns: - """ - pass # TODO diff --git a/minutes/phrase.py b/minutes/phrase.py new file mode 100644 index 0000000..dcc6ffe --- /dev/null +++ b/minutes/phrase.py @@ -0,0 +1,12 @@ + + +class Phrase: + def __init__(self, observation, speaker): + """[summary] + + Arguments: + observation {np.array} -- 1 dimensional audio sample. + speaker {[type]} -- Predicted Speaker. + """ + self.observation = observation + self.speaker = speaker diff --git a/test/config.py b/test/config.py index d62cbd4..2d4b9ad 100644 --- a/test/config.py +++ b/test/config.py @@ -4,7 +4,8 @@ import tempfile -from minutes import Speaker +from minutes import Speaker, Conversation + TEST_DIR = os.path.dirname(os.path.realpath(__file__)) ROOT_DIR = os.path.join(TEST_DIR, '..') @@ -12,6 +13,7 @@ SPEAKER1_AUDIO = os.path.join(FIXTURE_DIR, 'sample1.wav') SPEAKER2_AUDIO = os.path.join(FIXTURE_DIR, 'sample2.wav') +CONVERSATION_AUDIO = os.path.join(FIXTURE_DIR, 'conversation.wav') # Load speaker audio just once for all tests. SPEAKER1 = Speaker('speaker1') @@ -20,6 +22,9 @@ SPEAKER2 = Speaker('speaker2') SPEAKER2.add_audio(SPEAKER2_AUDIO) +# Create conversation just once for all tests. +CONVERSATION = Conversation(CONVERSATION_AUDIO) + @contextlib.contextmanager def cd(newdir, cleanup=None): diff --git a/test/test_conversation.py b/test/test_conversation.py new file mode 100644 index 0000000..604e51c --- /dev/null +++ b/test/test_conversation.py @@ -0,0 +1,6 @@ +import test.config as c + + +def test_conversation_observation_shape(): + obs = c.CONVERSATION.get_spectrograms(50) + assert obs.shape == (654, 129, 3) diff --git a/test/test_minutes.py b/test/test_minutes.py index 1c860a6..463c674 100644 --- a/test/test_minutes.py +++ b/test/test_minutes.py @@ -1,3 +1,5 @@ +import numpy as np + from minutes import Minutes import test.config as c @@ -15,3 +17,20 @@ def test_train(): def test_parents(): assert Minutes.parents == ['cnn'] + + +def test_phrases(): + for model_name in Minutes.parents: + minutes = Minutes(parent=model_name) + minutes.add_speaker(c.SPEAKER1) + minutes.add_speaker(c.SPEAKER2) + minutes.fit() + + # Predict new phrases. + phrases = minutes.phrases(c.CONVERSATION) + speakers = [p.speaker.name for p in phrases] + + ms_per_obs = minutes.parent.ms_per_observation + num_obs = c.CONVERSATION.get_observations(ms_per_obs) + assert len(phrases) == len(num_obs) + assert sorted(list(np.unique(speakers))) == ['speaker1', 'speaker2'] From a5e0e1ea742e477c14d1a029919001cc1ed374ec Mon Sep 17 00:00:00 2001 From: chadlagore Date: Thu, 31 May 2018 21:30:24 -0700 Subject: [PATCH 2/5] Refactored conversation and base to work maybe more harmoniously --- README.md | 11 +++------ minutes/__init__.py | 3 +-- minutes/audio.py | 38 ++++++++++------------------- minutes/base.py | 47 +++++++++++++++++++++--------------- minutes/conversation.py | 51 ++++++++++++++++++--------------------- minutes/phrase.py | 12 --------- minutes/speaker.py | 11 ++++++--- minutes/utils/keras.py | 2 +- test/config.py | 6 +---- test/test_audio.py | 2 +- test/test_conversation.py | 6 ----- test/test_minutes.py | 17 +++++++------ 12 files changed, 87 insertions(+), 119 deletions(-) delete mode 100644 minutes/phrase.py delete mode 100644 test/test_conversation.py diff --git a/README.md b/README.md index b47ca9b..569b8a6 100644 --- a/README.md +++ b/README.md @@ -6,9 +6,7 @@ [![Coverage Status](https://coveralls.io/repos/github/ubclaunchpad/minutes/badge.svg)](https://coveralls.io/github/ubclaunchpad/minutes) -Audio speaker diarization library. - -## Under Construction! +Audio speaker diarization library. ## :running: Development @@ -43,9 +41,6 @@ minutes.add_speakers([s1, s2]) # Fit the model. minutes.fit() -# Collect a new conversation for prediction. -conversation = Conversation('/path/to/conversation.wav') - -# Create phrases from the conversation. -phrases = minutes.phrases(conversation) +# Predict against a new conversation had by speakers s1 and s2. +conversation = Conversation('/path/to/conversation.wav', minutes) ``` diff --git a/minutes/__init__.py b/minutes/__init__.py index b3baccf..818f7df 100644 --- a/minutes/__init__.py +++ b/minutes/__init__.py @@ -1,4 +1,3 @@ from minutes.minutes import Minutes # noqa from minutes.speaker import Speaker # noqa -from minutes.conversation import Conversation # noqa -from minutes.phrase import Phrase # noqa +from minutes.conversation import Phrase, Conversation # noqa diff --git a/minutes/audio.py b/minutes/audio.py index 8f9947b..5093e02 100644 --- a/minutes/audio.py +++ b/minutes/audio.py @@ -8,6 +8,9 @@ class Audio: + """Internal audio maninpulation class. I reserve the right to change this + API :) + """ def __init__(self, audio_loc): if os.path.isdir(audio_loc): @@ -40,17 +43,19 @@ def samples_per_observation(self, ms_per_observation): """ return int(self.rate * ms_per_observation / 1000.) - def get_observations(self, ms_per_observation, verbose=False): + def get_observations(self, ms_per_observation): """Converts a internal raw audio vector into table with - one observations per row. + one spectrogram per row. Also returns raw observations. Arguments: ms_per_observation {int} -- The number of desired ms per obs. Returns: - np.array -- An array of observations, one per row. The width - of each row depends on the ms_per_observation, + raw -- The raw audio observation table. + processed -- An array of spectrograms, one per row. The width + of each spectrogram depends on the ms_per_observation. """ + # Reshape for processing into spectrograms. d = self.samples_per_observation(ms_per_observation) N = len(self.data) // d @@ -63,31 +68,14 @@ def get_observations(self, ms_per_observation, verbose=False): # Truncate last (partial) observation. data = data[:N * d] - if verbose: - t = len(self.data) - (N * d) - print('Truncating {} bytes from end of sample'.format(t)) - - # Reshape for processing into spectrograms. - return data.reshape((N, d)) - - def get_spectrograms(self, ms_per_observation, verbose=False): - """Converts a internal raw audio vector into table with - one spectrogram per row. - - Arguments: - ms_per_observation {int} -- The number of desired ms per obs. - - Returns: - np.array -- An array of spectrograms, one per row. The width - of each spectrogram depends on the ms_per_observation. - """ # Reshape for processing into spectrograms. - data = self.get_observations(ms_per_observation, verbose) + raw = data.reshape((N, d)) def spec_from_row(row): _, _, Sxx = signal.spectrogram(row) return Sxx # This is very slow! Perhaps some logging? - rows = (spec_from_row(row) for row in data) - return np.array([x for x in rows]) + rows = (spec_from_row(row) for row in raw) + processed = np.array([x for x in rows]) + return raw, processed diff --git a/minutes/base.py b/minutes/base.py index 253416f..8dff165 100644 --- a/minutes/base.py +++ b/minutes/base.py @@ -11,7 +11,6 @@ from sklearn.model_selection import train_test_split from minutes.models import MINUTES_BASE_MODEL_DIRECTORY -from minutes.phrase import Phrase class BaseModel: @@ -24,6 +23,19 @@ class BaseModel: 'random_state', } + @property + def preprocessing_params(self): + """Returns a mapping of parameters that are required to do preprocessing + of audio data suitable for this model. Useful as kwargs to audio + manipulation classes. + """ + return { + i: getattr(self, i) for i in self.intialization_params + if i in { + 'ms_per_observation', + } + } + @property def fitted(self): return self.model is not None @@ -98,8 +110,12 @@ def _generate_training_data(self): y -- a categorical one-hot encoding of different speakers numbered 1..k. """ - obs = [s.get_observations(self.ms_per_observation) - for s in self.speakers] + obs = [] + for s in self.speakers: + _, processed = s.get_observations(**self.preprocessing_params) + obs += processed, + + # Generate and flatten labels. labels = [[i] * len(o) for i, o in enumerate(obs)] flattened_labels = [j for i in labels for j in i] @@ -154,29 +170,20 @@ def save_model(self): if self.model is not None: self.model.save(os.path.join(self.home, 'keras.h5')) - def phrases(self, conversation): - """Predict against a new conversation. + def predict(self, observations): + """Predict against a table of audio observations. Arguments: - conversation {Conversation} -- A conversation built from an audio - sample. + observations {np.array} -- A table of processed audio observations. Returns: - List[Phrase] -- A list of Phrases. + np.array -- An array of predicted speakers. """ - # Predict against the conversation spectrograms. - X_hat = conversation.get_spectrograms(self.ms_per_observation) - result = self.model.predict(X_hat) + result = self.model.predict(observations) y_hat_indices = np.argmax(result, axis=1) - y_hat = np.array(self.speakers)[y_hat_indices] - - # Convert to a list of phrases. - # TODO: This is extra work, we already converted the - # observations when we called get_spectrograms. We can - # speed things up by somehow caching that result (or returning it) - # if it is prohibitably costly to regenerate these. - obs = conversation.get_observations(self.ms_per_observation) - return [Phrase(o, speaker) for o, speaker in zip(obs, y_hat)] + + # Index into the speaker array using the predicted speaker indicies. + return np.array(self.speakers)[y_hat_indices] def __str__(self): return self.name diff --git a/minutes/conversation.py b/minutes/conversation.py index 446fd82..20ef414 100644 --- a/minutes/conversation.py +++ b/minutes/conversation.py @@ -1,39 +1,36 @@ from minutes.audio import Audio -class Conversation: - - def __init__(self, audio_loc): - """Create a new conversation from audio sample. +class Phrase: + def __init__(self, observation, speaker): + """A phrase in a conversation, characterized by an audio segment + and a speaker. Arguments: - audio_loc {str} -- The absolute location of an audio conversation - sample. + observation {np.array} -- 1 dimensional audio sample. + speaker {Speaker} -- The inferred speaker for the audio segment. """ - self.audio = Audio(audio_loc) + self.observation = observation + self.speaker = speaker - def get_spectrograms(self, ms_per_observation, verbose=False): - """Converts the conversation audio sample into an n x d matrix of - spectrograms. - Keyword Arguments: - verbose {bool} -- (default: {False}) - ms_per_observation {int} -- (default: {False}) +class Conversation(Audio): - Returns: - np.array -- An n x d matrix of spectrograms. - """ - return self.audio.get_spectrograms(ms_per_observation, verbose) + def __init__(self, audio_loc, model): + """Create a new conversation from audio sample. - def get_observations(self, ms_per_observation, verbose=False): - """Converts the conversation audio sample into an n x d matrix of - observations. + Arguments: + audio_loc {str} -- The absolute location of an audio conversation + sample. + model {Minutes} -- A model trained on speakers within this + conversation. + """ + self.model = model + super().__init__(audio_loc) - Keyword Arguments: - verbose {bool} -- (default: {False}) - ms_per_observation {int} -- (default: {False}) + # Predict against the conversation spectrograms. + raw, X_hat = self.get_observations(**model.preprocessing_params) + y_hat = model.predict(X_hat) - Returns: - np.array -- An n x d matrix of observations. - """ - return self.audio.get_observations(ms_per_observation, verbose) + # Convert to a list of phrases. + self.phrases = [Phrase(o, speaker) for o, speaker in zip(raw, y_hat)] diff --git a/minutes/phrase.py b/minutes/phrase.py deleted file mode 100644 index dcc6ffe..0000000 --- a/minutes/phrase.py +++ /dev/null @@ -1,12 +0,0 @@ - - -class Phrase: - def __init__(self, observation, speaker): - """[summary] - - Arguments: - observation {np.array} -- 1 dimensional audio sample. - speaker {[type]} -- Predicted Speaker. - """ - self.observation = observation - self.speaker = speaker diff --git a/minutes/speaker.py b/minutes/speaker.py index ef327c3..b0dcea3 100644 --- a/minutes/speaker.py +++ b/minutes/speaker.py @@ -23,10 +23,13 @@ def add_audio(self, audio_loc): """ self.audio += Audio(audio_loc), - def get_observations(self, ms_per_observation, verbose=False): - obs = [a.get_spectrograms(ms_per_observation, verbose) - for a in self.audio] - return np.concatenate(obs) + def get_observations(self, **preprocessing_params): + raw, processed = [], [] + for a in self.audio: + r, p = a.get_observations(**preprocessing_params) + raw += r, + processed += p, + return np.concatenate(raw), np.concatenate(processed) def __eq__(self, other): return self.name == other.name diff --git a/minutes/utils/keras.py b/minutes/utils/keras.py index 155b97e..c61e16e 100644 --- a/minutes/utils/keras.py +++ b/minutes/utils/keras.py @@ -3,7 +3,7 @@ def copy_model(model): """Returns a copy of the model. - + Arguments: model {keras.Sequential} -- A model for copying. """ diff --git a/test/config.py b/test/config.py index 2d4b9ad..6cbc836 100644 --- a/test/config.py +++ b/test/config.py @@ -3,8 +3,7 @@ import shutil import tempfile - -from minutes import Speaker, Conversation +from minutes import Speaker TEST_DIR = os.path.dirname(os.path.realpath(__file__)) @@ -22,9 +21,6 @@ SPEAKER2 = Speaker('speaker2') SPEAKER2.add_audio(SPEAKER2_AUDIO) -# Create conversation just once for all tests. -CONVERSATION = Conversation(CONVERSATION_AUDIO) - @contextlib.contextmanager def cd(newdir, cleanup=None): diff --git a/test/test_audio.py b/test/test_audio.py index 77b8887..b3c625b 100644 --- a/test/test_audio.py +++ b/test/test_audio.py @@ -16,6 +16,6 @@ def test_samples_per_observation(): def test_get_spectrograms(): audio = Audio(c.SPEAKER1_AUDIO) - spec = audio.get_spectrograms(3000) + _, spec = audio.get_observations(3000) assert spec.shape == (5, 129, 214) assert spec.dtype == np.float64 diff --git a/test/test_conversation.py b/test/test_conversation.py deleted file mode 100644 index 604e51c..0000000 --- a/test/test_conversation.py +++ /dev/null @@ -1,6 +0,0 @@ -import test.config as c - - -def test_conversation_observation_shape(): - obs = c.CONVERSATION.get_spectrograms(50) - assert obs.shape == (654, 129, 3) diff --git a/test/test_minutes.py b/test/test_minutes.py index 463c674..fdd3332 100644 --- a/test/test_minutes.py +++ b/test/test_minutes.py @@ -1,6 +1,6 @@ import numpy as np -from minutes import Minutes +from minutes import Minutes, Conversation import test.config as c @@ -26,11 +26,12 @@ def test_phrases(): minutes.add_speaker(c.SPEAKER2) minutes.fit() - # Predict new phrases. - phrases = minutes.phrases(c.CONVERSATION) - speakers = [p.speaker.name for p in phrases] + # Predict new phrases (make sure we ony predict once per obs) + conversation = Conversation(c.CONVERSATION_AUDIO, minutes) + raw, _ = conversation.get_observations(**minutes.preprocessing_params) + assert len(conversation.phrases) == len(raw) + print(conversation.phrases) - ms_per_obs = minutes.parent.ms_per_observation - num_obs = c.CONVERSATION.get_observations(ms_per_obs) - assert len(phrases) == len(num_obs) - assert sorted(list(np.unique(speakers))) == ['speaker1', 'speaker2'] + # Make sure we ony predicted on speaker 1 and 2. + names = [p.speaker.name for p in conversation.phrases] + assert sorted(list(np.unique(names))) == ['speaker1', 'speaker2'] From 13b9a6b5ae8eda6b5867483384b865381c9e9619 Mon Sep 17 00:00:00 2001 From: chadlagore Date: Thu, 31 May 2018 22:09:20 -0700 Subject: [PATCH 3/5] Need my fixture --- test/fixtures/conversation.wav | Bin 0 -> 1046444 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 test/fixtures/conversation.wav diff --git a/test/fixtures/conversation.wav b/test/fixtures/conversation.wav new file mode 100644 index 0000000000000000000000000000000000000000..32bb26eeee6bd8563071a5b71027c077de991375 GIT binary patch literal 1046444 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Sep 17 00:00:00 2001 From: chadlagore <chad.d.lagore@gmail.com> Date: Sat, 16 Jun 2018 13:06:40 -0700 Subject: [PATCH 4/5] Fix non-deterministic test; update README to talk about determinism --- README.md | 19 +++++++++++++++++++ test/config.py | 7 +++++++ test/test_minutes.py | 6 +++--- 3 files changed, 29 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 569b8a6..d13d8ca 100644 --- a/README.md +++ b/README.md @@ -44,3 +44,22 @@ minutes.fit() # Predict against a new conversation had by speakers s1 and s2. conversation = Conversation('/path/to/conversation.wav', minutes) ``` + +## Reproducibility + +If you want to make sure your `Minutes` models are reprodicible, we recommend +setting the `numpy` and `tensorflow` state. + +```py +import numpy as np +import tensorflow as tf +from minutes import Minutes + +state = 42 +np.random.seed(state) +tf.set_random_seed(state) + +# Ensure the test data are generated deterministically by setting the Minutes +# state as well. +minutes = Minutes(parent='cnn', random_state=state) +``` diff --git a/test/config.py b/test/config.py index 6cbc836..e47b17b 100644 --- a/test/config.py +++ b/test/config.py @@ -3,6 +3,9 @@ import shutil import tempfile +import numpy as np +import tensorflow as tf + from minutes import Speaker @@ -21,6 +24,10 @@ SPEAKER2 = Speaker('speaker2') SPEAKER2.add_audio(SPEAKER2_AUDIO) +RANDOM_STATE = 42 +np.random.seed(RANDOM_STATE) +tf.set_random_seed(RANDOM_STATE) + @contextlib.contextmanager def cd(newdir, cleanup=None): diff --git a/test/test_minutes.py b/test/test_minutes.py index fdd3332..2ab8b75 100644 --- a/test/test_minutes.py +++ b/test/test_minutes.py @@ -30,8 +30,8 @@ def test_phrases(): conversation = Conversation(c.CONVERSATION_AUDIO, minutes) raw, _ = conversation.get_observations(**minutes.preprocessing_params) assert len(conversation.phrases) == len(raw) - print(conversation.phrases) - # Make sure we ony predicted on speaker 1 and 2. + # Make sure we predicted some subset of the acceptable values. names = [p.speaker.name for p in conversation.phrases] - assert sorted(list(np.unique(names))) == ['speaker1', 'speaker2'] + expected = {'speaker1', 'speaker2'} + assert set(np.unique(names)) <= expected From e0a4cb393ba7d6e4949338c549703eb7323c593e Mon Sep 17 00:00:00 2001 From: chadlagore <chad.d.lagore@gmail.com> Date: Sat, 16 Jun 2018 13:09:53 -0700 Subject: [PATCH 5/5] Typo; move preprocessing params to Audio class --- minutes/audio.py | 5 ++++- minutes/base.py | 5 ++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/minutes/audio.py b/minutes/audio.py index 5093e02..13e0192 100644 --- a/minutes/audio.py +++ b/minutes/audio.py @@ -6,9 +6,12 @@ import scipy.stats as stats import soundfile as sf +# Parameters for preprocessing audio files. +PREPROCESSING_PARAMS = {'ms_per_observation'} + class Audio: - """Internal audio maninpulation class. I reserve the right to change this + """Internal audio manipulation class. I reserve the right to change this API :) """ diff --git a/minutes/base.py b/minutes/base.py index 8dff165..9fe3c58 100644 --- a/minutes/base.py +++ b/minutes/base.py @@ -11,6 +11,7 @@ from sklearn.model_selection import train_test_split from minutes.models import MINUTES_BASE_MODEL_DIRECTORY +from minutes.audio import PREPROCESSING_PARAMS class BaseModel: @@ -31,9 +32,7 @@ def preprocessing_params(self): """ return { i: getattr(self, i) for i in self.intialization_params - if i in { - 'ms_per_observation', - } + if i in PREPROCESSING_PARAMS } @property