NoBackendError while getting features of audio files using librosa - python

Im using jupyter notebook for executing this. The Whole program is available in this link https://github.com/MiteshPuthran/Speech-Emotion-Analyzer/blob/master/final_results_gender_test.ipynb
I tried using ffmpeg, tried using another .wav file, nothing seems to be working. please help.
This is the code :
df = pd.DataFrame(columns=['feature'])
bookmark=0
for index,y in enumerate(mylist):
if mylist[index][6:-16]!='01' and mylist[index][6:-16]!='07' and mylist[index][6:-16]!='08' and mylist[index][:2]!='su' and mylist[index][:1]!='n' and mylist[index][:1]!='d':
X, sample_rate = librosa.load('C:/Users/Admin/Desktop/pw-4/Speech-Emotion-Analyzer-master/Speech-Emotion-Analyzer-master/'+y, res_type='kaiser_fast',duration=2.5,sr=22050*2,offset=0.5)
sample_rate = np.array(sample_rate)
mfccs = np.mean(librosa.feature.mfcc(y=X,
sr=sample_rate,
n_mfcc=13),
axis=0)
feature = mfccs
#[float(i) for i in feature]
#feature1=feature[:135]
df.loc[bookmark] = [feature]
bookmark=bookmark+1
and this is the error im getting:
RuntimeError Traceback (most recent call last)
File ~\AppData\Roaming\Python\Python39\site-packages\librosa\core\audio.py:155, in load(path, sr, mono, offset, duration, dtype, res_type)
153 else:
154 # Otherwise, create the soundfile object
--> 155 context = sf.SoundFile(path)
157 with context as sf_desc:
File ~\AppData\Roaming\Python\Python39\site-packages\soundfile.py:629, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd)
627 self._info = _create_info_struct(file, mode, samplerate, channels,
628 format, subtype, endian)
--> 629 self._file = self._open(file, mode_int, closefd)
630 if set(mode).issuperset('r+') and self.seekable():
631 # Move write position to 0 (like in Python file objects)
File ~\AppData\Roaming\Python\Python39\site-packages\soundfile.py:1183, in SoundFile._open(self, file, mode_int, closefd)
1182 raise TypeError("Invalid file: {0!r}".format(self.name))
-> 1183 _error_check(_snd.sf_error(file_ptr),
1184 "Error opening {0!r}: ".format(self.name))
1185 if mode_int == _snd.SFM_WRITE:
1186 # Due to a bug in libsndfile version <= 1.0.25, frames != 0
1187 # when opening a named pipe in SFM_WRITE mode.
1188 # See http://github.com/erikd/libsndfile/issues/77.
File ~\AppData\Roaming\Python\Python39\site-packages\soundfile.py:1357, in _error_check(err, prefix)
1356 err_str = _snd.sf_error_number(err)
-> 1357 raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace'))
RuntimeError: Error opening 'C:/Users/Admin/Desktop/pw-4/Speech-Emotion-Analyzer-master/Speech-Emotion-Analyzer-master/AudioRecorder.ipynb': File contains data in an unknown format.
During handling of the above exception, another exception occurred:
NoBackendError Traceback (most recent call last)
Input In [46], in <cell line: 3>()
3 for index,y in enumerate(mylist):
4 if mylist[index][6:-16]!='01' and mylist[index][6:-16]!='07' and mylist[index][6:-16]!='08' and mylist[index][:2]!='su' and mylist[index][:1]!='n' and mylist[index][:1]!='d':
----> 5 X, sample_rate = librosa.load('C:/Users/Admin/Desktop/pw-4/Speech-Emotion-Analyzer-master/Speech-Emotion-Analyzer-master/'+y, res_type='kaiser_fast',duration=2.5,sr=22050*2,offset=0.5)
6 sample_rate = np.array(sample_rate)
7 mfccs = np.mean(librosa.feature.mfcc(y=X,
8 sr=sample_rate,
9 n_mfcc=13),
10 axis=0)
File ~\AppData\Roaming\Python\Python39\site-packages\librosa\util\decorators.py:88, in deprecate_positional_args.<locals>._inner_deprecate_positional_args.<locals>.inner_f(*args, **kwargs)
86 extra_args = len(args) - len(all_args)
87 if extra_args <= 0:
---> 88 return f(*args, **kwargs)
90 # extra_args > 0
91 args_msg = [
92 "{}={}".format(name, arg)
93 for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:])
94 ]
File ~\AppData\Roaming\Python\Python39\site-packages\librosa\core\audio.py:174, in load(path, sr, mono, offset, duration, dtype, res_type)
172 if isinstance(path, (str, pathlib.PurePath)):
173 warnings.warn("PySoundFile failed. Trying audioread instead.", stacklevel=2)
--> 174 y, sr_native = __audioread_load(path, offset, duration, dtype)
175 else:
176 raise (exc)
File ~\AppData\Roaming\Python\Python39\site-packages\librosa\core\audio.py:198, in __audioread_load(path, offset, duration, dtype)
192 """Load an audio buffer using audioread.
193
194 This loads one block at a time, and then concatenates the results.
195 """
197 y = []
--> 198 with audioread.audio_open(path) as input_file:
199 sr_native = input_file.samplerate
200 n_channels = input_file.channels
File ~\AppData\Roaming\Python\Python39\site-packages\audioread\__init__.py:116, in audio_open(path, backends)
113 pass
115 # All backends failed!
--> 116 raise NoBackendError()
NoBackendError:

Related

Error opening file because of an unknown format

I read some Audio file, labeled them, and together with their path, save the path and emotion of each Audioo file in a csv file. Now I want to read their path from the file and open them but I get this Error:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
File ~\.conda\envs\nhashemi\lib\site-packages\librosa\core\audio.py:155, in load(path, sr, mono, offset, duration, dtype, res_type)
153 else:
154 # Otherwise, create the soundfile object
--> 155 context = sf.SoundFile(path)
157 with context as sf_desc:
File ~\.conda\envs\nhashemi\lib\site-packages\soundfile.py:629, in SoundFile.__init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd)
627 self._info = _create_info_struct(file, mode, samplerate, channels,
628 format, subtype, endian)
--> 629 self._file = self._open(file, mode_int, closefd)
630 if set(mode).issuperset('r+') and self.seekable():
631 # Move write position to 0 (like in Python file objects)
File ~\.conda\envs\nhashemi\lib\site-packages\soundfile.py:1183, in SoundFile._open(self, file, mode_int, closefd)
1182 raise TypeError("Invalid file: {0!r}".format(self.name))
-> 1183 _error_check(_snd.sf_error(file_ptr),
1184 "Error opening {0!r}: ".format(self.name))
1185 if mode_int == _snd.SFM_WRITE:
1186 # Due to a bug in libsndfile version <= 1.0.25, frames != 0
1187 # when opening a named pipe in SFM_WRITE mode.
1188 # See http://github.com/erikd/libsndfile/issues/77.
File ~\.conda\envs\nhashemi\lib\site-packages\soundfile.py:1357, in _error_check(err, prefix)
1356 err_str = _snd.sf_error_number(err)
-> 1357 raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace'))
RuntimeError: Error opening 'C:/Users/external_dipf/Documents/Dataset/CREMA/AudioWAV/1001_IEO_FEA_HI.wav': File contains data in an unknown format.
During handling of the above exception, another exception occurred:
NoBackendError Traceback (most recent call last)
Input In [553], in <cell line: 3>()
1 emotion='fear'
2 path = np.array(data_path.Path[data_path.Emotions==emotion])[1]
----> 3 data, sampling_rate = librosa.load(path)
4 create_waveplot(data, sampling_rate, emotion)
5 create_spectrogram(data, sampling_rate, emotion)
File ~\.conda\envs\nhashemi\lib\site-packages\librosa\util\decorators.py:88, in deprecate_positional_args.<locals>._inner_deprecate_positional_args.<locals>.inner_f(*args, **kwargs)
86 extra_args = len(args) - len(all_args)
87 if extra_args <= 0:
---> 88 return f(*args, **kwargs)
90 # extra_args > 0
91 args_msg = [
92 "{}={}".format(name, arg)
93 for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:])
94 ]
File ~\.conda\envs\nhashemi\lib\site-packages\librosa\core\audio.py:174, in load(path, sr, mono, offset, duration, dtype, res_type)
172 if isinstance(path, (str, pathlib.PurePath)):
173 warnings.warn("PySoundFile failed. Trying audioread instead.", stacklevel=2)
--> 174 y, sr_native = __audioread_load(path, offset, duration, dtype)
175 else:
176 raise (exc)
File ~\.conda\envs\nhashemi\lib\site-packages\librosa\core\audio.py:198, in __audioread_load(path, offset, duration, dtype)
192 """Load an audio buffer using audioread.
193
194 This loads one block at a time, and then concatenates the results.
195 """
197 y = []
--> 198 with audioread.audio_open(path) as input_file:
199 sr_native = input_file.samplerate
200 n_channels = input_file.channels
File ~\.conda\envs\nhashemi\lib\site-packages\audioread\__init__.py:116, in audio_open(path, backends)
113 pass
115 # All backends failed!
--> 116 raise NoBackendError()
NoBackendError:
Here is my code to label and specify the label (emotion) of each file
CREMA ="C:/Users/external_dipf/Documents/Dataset/CREMA/AudioWAV/"
crema_directory_list = os.listdir(CREMA)
file_emotion = []
file_path = []
for file in crema_directory_list:
# storing file paths
file_path.append(CREMA + file)
# storing file emotions
part=file.split('_')
if part[2] == 'SAD':
file_emotion.append('sad')
elif part[2] == 'ANG':
file_emotion.append('angry')
elif part[2] == 'DIS':
file_emotion.append('disgust')
elif part[2] == 'FEA':
file_emotion.append('fear')
elif part[2] == 'HAP':
file_emotion.append('happy')
elif part[2] == 'NEU':
file_emotion.append('neutral')
else:
file_emotion.append('Unknown')
# dataframe for emotion of files
emotion_df = pd.DataFrame(file_emotion, columns=['Emotions'])
# dataframe for path of files.
path_df = pd.DataFrame(file_path, columns=['Path'])
CREMA_df = pd.concat([emotion_df, path_df], axis=1)
CREMA_df.head()
Here is were I save them in a CSV file
data_path = pd.concat([CREMA_df, RAVDESS_df, TESS_df, SAVEE_df], axis = 0)
data_path.to_csv("data_path.csv",index=False)
data_path.head()
and here I am trying to read the file. The error is related to the CREMA dataset.
emotion='fear'
path = np.array(data_path.Path[data_path.Emotions==emotion])[1]
data, sampling_rate = librosa.load(path)
create_waveplot(data, sampling_rate, emotion)
create_spectrogram(data, sampling_rate, emotion)
Audio(path)
I checked the path file, everything was correct. I can open other wav files. My librosa version is 0.9.1

Image processing in Tensor flow TFX pipelines

I am trying to get a Tensorflow TFX pipeline up and running using the MNIST dataset.
# Imports
import pandas as pd
import numpy as np
from keras.datasets import mnist
import tensorflow as tf
from tfx import v1 as tfx
import os
from tfx.components import ImportExampleGen
from platform import python_version
python_version() #'3.8.8'
# Load the data - 60,000 training examples and 10,000 testing examples
(train_x, train_y), (test_x, test_y) = mnist.load_data()
Setup pipeline paths
_pipeline_root = './pipeline'
_data_root = './data'
if not os.path.isdir(_pipeline_root) and not os.path.isdir(_data_root):
!mkdir {_pipeline_root}
!mkdir {_data_root}
Write the data to TF.record format and save in eval and train dirs. NOTE that the MNIST data starts as a numpy array 28x28 and is converted to a bytestring to enable it to be encoded as part of the Tf.record.
def _bytes_feature(value):
"""Returns a bytes_list from a string / byte."""
if isinstance(value, type(tf.constant(0))): # if value ist tensor
value = value.numpy() # get value of tensor
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _int64_feature(value):
"""Returns an int64_list from a bool / enum / int / uint."""
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def serialize_array(array):
array = tf.io.serialize_tensor(array)
return array
def image_label_to_tf_train(image, label):
image_shape = np.shape(image)
#define the dictionary -- the structure -- of our single example
data = {
'height': _int64_feature(image_shape[0]),
'width': _int64_feature(image_shape[1]),
'raw_image' : _bytes_feature(serialize_array(image)),
'label' : _int64_feature(label)
}
#create an Example, wrapping the single features
return tf.train.Example(features=tf.train.Features(feature=data))
def write_images_to_tfr_short(images, labels, filename:str="images", folder = ""):
if not os.path.isdir(folder):
!mkdir {folder}
filename= folder + "/" + filename+".tfrecords"
writer = tf.io.TFRecordWriter(filename) #create a writer that'll store our data to disk
count = 0
for index in range(len(images)):
#get the data we want to write
current_image = images[index]
current_label = labels[index]
out = image_label_to_tf_train(image=current_image, label=current_label)
writer.write(out.SerializeToString())
count += 1
writer.close()
print(f"Wrote {count} elements to TFRecord")
return count
The next stage is to call the transform component which uses the preprocessing_fn. This function should process all the data so for example divide the image array by 255 is a standard feature process. But the image is still as a bytestring and I can't for the life of me figure out how to turn it back into an array. The below is what I have tried.
def preprocessing_fn(inputs):
"""tf.transform's callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
"""
# Initialize outputs dictionary
outputs = {}
raw_image_dataset = inputs[_IMAGE_KEY]
img = tf.io.decode_raw(raw_image_dataset, tf.int64)
outputs[_IMAGE_KEY] = img
outputs[_LABEL_KEY] = tf.cast(inputs[_LABEL_KEY], tf.int64)
return outputs
I get the following error:
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.
WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.
WARNING:root:Make sure that locally built Python SDK docker image has Python 3.8 interpreter.
INFO:tensorflow:Assets written to: ./pipeline/Transform/transform_graph/225/.temp_path/tftransform_tmp/26150ae80de847fab932efeb0f0c610f/assets
INFO:tensorflow:Assets written to: ./pipeline/Transform/transform_graph/225/.temp_path/tftransform_tmp/26150ae80de847fab932efeb0f0c610f/assets
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()
/opt/conda/lib/python3.8/site-packages/apache_beam/transforms/core.py in <lambda>(x, *args, **kwargs)
1636 if fn_takes_side_inputs(fn):
-> 1637 wrapper = lambda x, *args, **kwargs: [fn(x, *args, **kwargs)]
1638 else:
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/beam/impl.py in _create_v2_saved_model(tensor_replacement_map, base_temp_dir, preprocessing_fn, input_signature, baseline_analyzers_fingerprint, output_keys_to_name_map)
662 saved_model_dir = beam_common.get_unique_temp_path(base_temp_dir)
--> 663 impl_helper.trace_and_write_v2_saved_model(saved_model_dir, preprocessing_fn,
664 input_signature, base_temp_dir,
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/impl_helper.py in trace_and_write_v2_saved_model(saved_model_dir, preprocessing_fn, input_signature, base_temp_dir, baseline_analyzers_fingerprint, tensor_replacement_map, output_keys_to_name_map)
893 analyzer_nodes.TENSOR_REPLACEMENTS):
--> 894 metadata = _trace_and_get_metadata(concrete_transform_fn, structured_inputs,
895 preprocessing_fn, base_temp_dir,
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/impl_helper.py in _trace_and_get_metadata(concrete_transform_fn, structured_inputs, preprocessing_fn, base_temp_dir, tensor_replacement_map)
805 return dataset_metadata.DatasetMetadata(
--> 806 schema=schema_inference.infer_feature_schema_v2(
807 concrete_transform_fn.structured_outputs,
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/schema_inference.py in infer_feature_schema_v2(features, concrete_metadata_fn, evaluate_schema_overrides)
255 metadata)
--> 256 return _infer_feature_schema_common(
257 features,
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/schema_inference.py in _infer_feature_schema_common(features, tensor_ranges, feature_annotations, global_annotations, is_evaluation_complete)
300 min=min_value, max=max_value, is_categorical=True)
--> 301 feature_spec = _feature_spec_from_batched_tensors(features,
302 is_evaluation_complete)
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/schema_inference.py in _feature_spec_from_batched_tensors(tensors, is_evaluation_complete)
128 dim is None for dim in shape.as_list()[1:]):
--> 129 raise ValueError(
130 'Feature {} ({}) had invalid shape {} for FixedLenFeature: apart '
ValueError: Feature raw_image (Tensor("Identity_1:0", shape=(None, 1, None), dtype=int64)) had invalid shape (None, 1, None) for FixedLenFeature: apart from the batch dimension, all dimensions must have known size
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-37-7beafa4fe436> in <module>
3 schema=schema_gen.outputs['schema'],
4 module_file=os.path.abspath(_mnist_transform_module))
----> 5 context.run(transform, enable_cache=False)
/opt/conda/lib/python3.8/site-packages/tfx/orchestration/experimental/interactive/interactive_context.py in run_if_ipython(*args, **kwargs)
61 # __IPYTHON__ variable is set by IPython, see
62 # https://ipython.org/ipython-doc/rel-0.10.2/html/interactive/reference.html#embedding-ipython.
---> 63 return fn(*args, **kwargs)
64 else:
65 absl.logging.warning(
/opt/conda/lib/python3.8/site-packages/tfx/orchestration/experimental/interactive/interactive_context.py in run(self, component, enable_cache, beam_pipeline_args)
181 telemetry_utils.LABEL_TFX_RUNNER: runner_label,
182 }):
--> 183 execution_id = launcher.launch().execution_id
184
185 return execution_result.ExecutionResult(
/opt/conda/lib/python3.8/site-packages/tfx/orchestration/launcher/base_component_launcher.py in launch(self)
198 # be immutable in this context.
199 # output_dict can still be changed, specifically properties.
--> 200 self._run_executor(execution_decision.execution_id,
201 copy.deepcopy(execution_decision.input_dict),
202 execution_decision.output_dict,
/opt/conda/lib/python3.8/site-packages/tfx/orchestration/launcher/in_process_component_launcher.py in _run_executor(self, execution_id, input_dict, output_dict, exec_properties)
71 # be immutable in this context.
72 # output_dict can still be changed, specifically properties.
---> 73 executor.Do(
74 copy.deepcopy(input_dict), output_dict, copy.deepcopy(exec_properties))
/opt/conda/lib/python3.8/site-packages/tfx/components/transform/executor.py in Do(self, input_dict, output_dict, exec_properties)
581 # remove the `_pip_dependencies` attribute.
582 with udf_utils.TempPipInstallContext(self._pip_dependencies):
--> 583 TransformProcessor().Transform(label_inputs, label_outputs, status_file)
584 logging.debug('Cleaning up temp path %s on executor success', temp_path)
585 io_utils.delete_dir(temp_path)
/opt/conda/lib/python3.8/site-packages/tfx/components/transform/executor.py in Transform(***failed resolving arguments***)
1114 materialization_format = (
1115 transform_paths_file_formats[-1] if materialize_output_paths else None)
-> 1116 self._RunBeamImpl(analyze_data_list, transform_data_list, preprocessing_fn,
1117 stats_options_updater_fn, force_tf_compat_v1,
1118 input_dataset_metadata, transform_output_path,
/opt/conda/lib/python3.8/site-packages/tfx/components/transform/executor.py in _RunBeamImpl(self, analyze_data_list, transform_data_list, preprocessing_fn, stats_options_updater_fn, force_tf_compat_v1, input_dataset_metadata, transform_output_path, raw_examples_data_format, temp_path, input_cache_dir, output_cache_dir, disable_statistics, per_set_stats_output_paths, materialization_format, analyze_paths_count, stats_output_paths, make_beam_pipeline_fn)
1496 for dataset in transform_data_list:
1497 infix = 'TransformIndex{}'.format(dataset.index)
-> 1498 (dataset.transformed
1499 | 'EncodeAndSerialize[{}]'.format(infix) >> beam.ParDo(
1500 self._RecordBatchToExamplesFn(transformed_schema_proto))
/opt/conda/lib/python3.8/site-packages/apache_beam/pipeline.py in __exit__(self, exc_type, exc_val, exc_tb)
594 try:
595 if not exc_type:
--> 596 self.result = self.run()
597 self.result.wait_until_finish()
598 finally:
/opt/conda/lib/python3.8/site-packages/apache_beam/pipeline.py in run(self, test_runner_api)
571 finally:
572 shutil.rmtree(tmpdir)
--> 573 return self.runner.run_pipeline(self, self._options)
574 finally:
575 if not is_in_ipython():
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/direct/direct_runner.py in run_pipeline(self, pipeline, options)
129 runner = BundleBasedDirectRunner()
130
--> 131 return runner.run_pipeline(pipeline, options)
132
133
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py in run_pipeline(self, pipeline, options)
197 options.view_as(pipeline_options.ProfilingOptions))
198
--> 199 self._latest_run_result = self.run_via_runner_api(
200 pipeline.to_runner_api(default_environment=self._default_environment))
201 return self._latest_run_result
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py in run_via_runner_api(self, pipeline_proto)
208 # TODO(pabloem, BEAM-7514): Create a watermark manager (that has access to
209 # the teststream (if any), and all the stages).
--> 210 return self.run_stages(stage_context, stages)
211
212 #contextlib.contextmanager
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py in run_stages(self, stage_context, stages)
393 )
394
--> 395 stage_results = self._run_stage(
396 runner_execution_context, bundle_context_manager)
397
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py in _run_stage(self, runner_execution_context, bundle_context_manager)
658 while True:
659 last_result, deferred_inputs, fired_timers, watermark_updates = (
--> 660 self._run_bundle(
661 runner_execution_context,
662 bundle_context_manager,
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py in _run_bundle(self, runner_execution_context, bundle_context_manager, data_input, data_output, input_timers, expected_timer_output, bundle_manager)
781 expected_timer_output)
782
--> 783 result, splits = bundle_manager.process_bundle(
784 data_input, data_output, input_timers, expected_timer_output)
785 # Now we collect all the deferred inputs remaining from bundle execution.
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/fn_runner.py in process_bundle(self, inputs, expected_outputs, fired_timers, expected_output_timers, dry_run)
1092 process_bundle_descriptor.id,
1093 cache_tokens=[next(self._cache_token_generator)]))
-> 1094 result_future = self._worker_handler.control_conn.push(process_bundle_req)
1095
1096 split_results = [] # type: List[beam_fn_api_pb2.ProcessBundleSplitResponse]
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/portability/fn_api_runner/worker_handlers.py in push(self, request)
376 self._uid_counter += 1
377 request.instruction_id = 'control_%s' % self._uid_counter
--> 378 response = self.worker.do_instruction(request)
379 return ControlFuture(request.instruction_id, response)
380
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/sdk_worker.py in do_instruction(self, request)
578 if request_type:
579 # E.g. if register is set, this will call self.register(request.register))
--> 580 return getattr(self, request_type)(
581 getattr(request, request_type), request.instruction_id)
582 else:
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/sdk_worker.py in process_bundle(self, request, instruction_id)
616 with self.maybe_profile(instruction_id):
617 delayed_applications, requests_finalization = (
--> 618 bundle_processor.process_bundle(instruction_id))
619 monitoring_infos = bundle_processor.monitoring_infos()
620 monitoring_infos.extend(self.state_cache_metrics_fn())
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/bundle_processor.py in process_bundle(self, instruction_id)
993 element.timer_family_id, timer_data)
994 elif isinstance(element, beam_fn_api_pb2.Elements.Data):
--> 995 input_op_by_transform_id[element.transform_id].process_encoded(
996 element.data)
997
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/bundle_processor.py in process_encoded(self, encoded_windowed_values)
219 decoded_value = self.windowed_coder_impl.decode_from_stream(
220 input_stream, True)
--> 221 self.output(decoded_value)
222
223 def monitoring_infos(self, transform_id, tag_to_pcollection_id):
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/operations.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/operations.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.Operation.output()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/operations.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/operations.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/operations.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.SimpleInvoker.invoke_process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common._OutputProcessor.process_outputs()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/operations.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/operations.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/operations.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.SimpleInvoker.invoke_process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common._OutputProcessor.process_outputs()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/operations.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.SingletonConsumerSet.receive()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/operations.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/worker/operations.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.worker.operations.DoOperation.process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner._reraise_augmented()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.DoFnRunner.process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker.invoke_process()
/opt/conda/lib/python3.8/site-packages/apache_beam/runners/common.cpython-38-x86_64-linux-gnu.so in apache_beam.runners.common.PerWindowInvoker._invoke_process_per_window()
/opt/conda/lib/python3.8/site-packages/apache_beam/transforms/core.py in <lambda>(x, *args, **kwargs)
1635 from apache_beam.transforms.util import fn_takes_side_inputs
1636 if fn_takes_side_inputs(fn):
-> 1637 wrapper = lambda x, *args, **kwargs: [fn(x, *args, **kwargs)]
1638 else:
1639 wrapper = lambda x: [fn(x)]
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/beam/impl.py in _create_v2_saved_model(tensor_replacement_map, base_temp_dir, preprocessing_fn, input_signature, baseline_analyzers_fingerprint, output_keys_to_name_map)
661 """
662 saved_model_dir = beam_common.get_unique_temp_path(base_temp_dir)
--> 663 impl_helper.trace_and_write_v2_saved_model(saved_model_dir, preprocessing_fn,
664 input_signature, base_temp_dir,
665 baseline_analyzers_fingerprint,
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/impl_helper.py in trace_and_write_v2_saved_model(saved_model_dir, preprocessing_fn, input_signature, base_temp_dir, baseline_analyzers_fingerprint, tensor_replacement_map, output_keys_to_name_map)
892 if not concrete_transform_fn.graph.get_collection(
893 analyzer_nodes.TENSOR_REPLACEMENTS):
--> 894 metadata = _trace_and_get_metadata(concrete_transform_fn, structured_inputs,
895 preprocessing_fn, base_temp_dir,
896 tensor_replacement_map)
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/impl_helper.py in _trace_and_get_metadata(concrete_transform_fn, structured_inputs, preprocessing_fn, base_temp_dir, tensor_replacement_map)
804 evaluate_schema_overrides=True)
805 return dataset_metadata.DatasetMetadata(
--> 806 schema=schema_inference.infer_feature_schema_v2(
807 concrete_transform_fn.structured_outputs,
808 concrete_metadata_fn,
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/schema_inference.py in infer_feature_schema_v2(features, concrete_metadata_fn, evaluate_schema_overrides)
254 tensor_annotations, global_annotations = _get_schema_annotations_v2(
255 metadata)
--> 256 return _infer_feature_schema_common(
257 features,
258 tensor_ranges,
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/schema_inference.py in _infer_feature_schema_common(features, tensor_ranges, feature_annotations, global_annotations, is_evaluation_complete)
299 domains[name] = schema_pb2.IntDomain(
300 min=min_value, max=max_value, is_categorical=True)
--> 301 feature_spec = _feature_spec_from_batched_tensors(features,
302 is_evaluation_complete)
303
/opt/conda/lib/python3.8/site-packages/tensorflow_transform/schema_inference.py in _feature_spec_from_batched_tensors(tensors, is_evaluation_complete)
127 if is_evaluation_complete and any(
128 dim is None for dim in shape.as_list()[1:]):
--> 129 raise ValueError(
130 'Feature {} ({}) had invalid shape {} for FixedLenFeature: apart '
131 'from the batch dimension, all dimensions must have known size'
ValueError: Feature raw_image (Tensor("Identity_1:0", shape=(None, 1, None), dtype=int64)) had invalid shape (None, 1, None) for FixedLenFeature: apart from the batch dimension, all dimensions must have known size [while running 'Analyze/CreateSavedModel[tf_v2_only]/CreateSavedModel']
I know the label feature is working as I can call the below code and get a print as so....
transform = tfx.components.Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
module_file=os.path.abspath(_mnist_transform_module))
context.run(transform, enable_cache=False)
# Get the URI of the output artifact representing the transformed examples
train_uri = os.path.join(transform.outputs['transformed_examples'].get()[0].uri, 'Split-train')
# Get the list of files in this directory (all compressed TFRecord files)
tfrecord_filenames = [os.path.join(train_uri, name)
for name in os.listdir(train_uri)]
# Create a `TFRecordDataset` to read these files
dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type="GZIP")
# Decode the first record and print output
for tfrecord in dataset.take(1):
serialized_example = tfrecord.numpy()
example = tf.train.Example()
example.ParseFromString(serialized_example)
print(example)
IF I remove the lines:
img = tf.io.decode_raw(raw_image_dataset, tf.int64)
outputs[_IMAGE_KEY] = img
I get printed
features {
feature {
key: "label"
value {
int64_list {
value: 5
}
}
}
}
This shows what I am doing to the label feature is working but I really can't figure how to transform the image bytes. Part of the issue is I'm not completely sure what the format is as it's just a tensor which is pretty opaque. It seems given the label operation I'm operating on a column of data effectively but again, can't figure the correct operation or syntax

For any future viewers this works
raw_image_dataset = tf.map_fn(fn = lambda x : tf.io.parse_tensor(x[0], tf.uint8, name=None), elems = raw_image_dataset, fn_output_signature = tf.TensorSpec((28,28),dtype=tf.uint8, name=None), infer_shape = True)
raw_image_dataset = tf.cast(raw_image_dataset, tf.int64)
outputs[_IMAGE_KEY] = raw_image_dataset

So I think I solved this using
raw_image_dataset = inputs[_IMAGE_KEY]
raw_image_dataset = tf.map_fn(fn = lambda x : tf.io.decode_image(x[0]) , elems = raw_image_dataset, dtype=tf.uint8)
Theres something about the data going in as a batch so needing to map it and also using the right component of the resulting tensor "x[0]", I'm still not 100% sure on why this is the case but it seems to run.
Now I'm struggling with TFX as it won't let me output features that are different to the what went in...

Split multi-page tiff with pillow package

I have searched for that topic and I found the following code
from PIL import Image, ImageSequence
im = Image.open("MySample.tiff")
for i, page in enumerate(ImageSequence.Iterator(im)):
page.save("Page_%d.png" % i)
The code works for some tiff files but I encountered an error with one of them
The traceback is like that
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-input-2-12bbb3a559f8> in <module>
5 im = Image.open("MySample.tiff")
6 for i, page in enumerate(ImageSequence.Iterator(im)):
----> 7 page.save("Page_%d.png" % i)
C:\ProgramData\Anaconda3\lib\site-packages\PIL\Image.py in save(self, fp, format, **params)
2128
2129 # may mutate self!
-> 2130 self._ensure_mutable()
2131
2132 save_all = params.pop("save_all", False)
C:\ProgramData\Anaconda3\lib\site-packages\PIL\Image.py in _ensure_mutable(self)
617 def _ensure_mutable(self):
618 if self.readonly:
--> 619 self._copy()
620 else:
621 self.load()
C:\ProgramData\Anaconda3\lib\site-packages\PIL\Image.py in _copy(self)
610
611 def _copy(self):
--> 612 self.load()
613 self.im = self.im.copy()
614 self.pyaccess = None
C:\ProgramData\Anaconda3\lib\site-packages\PIL\TiffImagePlugin.py in load(self)
1086 def load(self):
1087 if self.tile and self.use_load_libtiff:
-> 1088 return self._load_libtiff()
1089 return super().load()
1090
C:\ProgramData\Anaconda3\lib\site-packages\PIL\TiffImagePlugin.py in _load_libtiff(self)
1190
1191 if err < 0:
-> 1192 raise OSError(err)
1193
1194 return Image.Image.load(self)
OSError: -9

How can I process OPUS format with Librosa?

I am trying to generate spectrograms by using Librosa. When I was working with the .wav format file it was working fine. But I changed the format to OPUS audio codec and tried to run the same file, it give me below error.
X, sample_rate = librosa.load('TESS emotion datasets opus/OAF_Fear/OAF_beg_fear.opus', res_type='kaiser_fast', duration = 2.5, sr = 22050*2, offset = 0.5)
Error generated:
RuntimeError Traceback (most recent call last)
~/anaconda3/lib/python3.6/site-packages/librosa/core/audio.py in load(path, sr, mono, offset, duration, dtype, res_type)
145 try:
--> 146 with sf.SoundFile(path) as sf_desc:
147 sr_native = sf_desc.samplerate
~/anaconda3/lib/python3.6/site-packages/soundfile.py in __init__(self, file, mode, samplerate, channels, subtype, endian, format, closefd)
628 format, subtype, endian)
--> 629 self._file = self._open(file, mode_int, closefd)
630 if set(mode).issuperset('r+') and self.seekable():
~/anaconda3/lib/python3.6/site-packages/soundfile.py in _open(self, file, mode_int, closefd)
1183 _error_check(_snd.sf_error(file_ptr),
-> 1184 "Error opening {0!r}: ".format(self.name))
1185 if mode_int == _snd.SFM_WRITE:
~/anaconda3/lib/python3.6/site-packages/soundfile.py in _error_check(err, prefix)
1356 err_str = _snd.sf_error_number(err)
-> 1357 raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace'))
1358
RuntimeError: Error opening 'TESS emotion datasets opus/OAF_Fear/OAF_beg_fear.opus': File contains data in an unimplemented format.
During handling of the above exception, another exception occurred:
NoBackendError Traceback (most recent call last)
<ipython-input-39-1372f02f676e> in <module>()
----> 1 X, sample_rate = librosa.load('TESS emotion datasets opus/OAF_Fear/OAF_beg_fear.opus', res_type='kaiser_fast', duration = 2.5, sr = 22050*2, offset = 0.5)
~/anaconda3/lib/python3.6/site-packages/librosa/core/audio.py in load(path, sr, mono, offset, duration, dtype, res_type)
161 if isinstance(path, (str, pathlib.PurePath)):
162 warnings.warn("PySoundFile failed. Trying audioread instead.")
--> 163 y, sr_native = __audioread_load(path, offset, duration, dtype)
164 else:
165 raise (exc)
~/anaconda3/lib/python3.6/site-packages/librosa/core/audio.py in __audioread_load(path, offset, duration, dtype)
185
186 y = []
--> 187 with audioread.audio_open(path) as input_file:
188 sr_native = input_file.samplerate
189 n_channels = input_file.channels
~/anaconda3/lib/python3.6/site-packages/audioread/__init__.py in audio_open(path, backends)
114
115 # All backends failed!
--> 116 raise NoBackendError()
NoBackendError:
I tried to install ffmpeg and gstreamer as suggested by some previous answers and github page of Librosa. But it didn't solve the problem.
On the contrary, this audio format works well when I run the same code in Google Colab.
What can be the reason of this error? How to solve it?

Word2Vec error when loading in GoogleNews data

I am following a tutorial here: https://towardsdatascience.com/multi-class-text-classification-model-comparison-and-selection-5eb066197568
I am at the part "Word2vec and Logistic Regression". I have downloaded the "GoogleNews-vectors-negative300.bin.gz" file and I am tyring to apply it to my own text data. However when I get to the following code:
%%time
from gensim.models import Word2Vec
wv = gensim.models.KeyedVectors.load_word2vec_format("/data/users/USERS/File_path/classifier/GoogleNews_Embedding/GoogleNews-vectors-negative300.bin.gz", binary=True)
wv.init_sims(replace=True)
I run into the following error:
/data/users/msmith/env/lib64/python3.6/site-packages/smart_open/smart_open_lib.py:398: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function
'See the migration notes for details: %s' % _MIGRATION_NOTES_URL
---------------------------------------------------------------------------
EOFError Traceback (most recent call last)
<timed exec> in <module>
~/env/lib64/python3.6/site-packages/gensim/models/keyedvectors.py in load_word2vec_format(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype)
1492 return _load_word2vec_format(
1493 cls, fname, fvocab=fvocab, binary=binary, encoding=encoding, unicode_errors=unicode_errors,
-> 1494 limit=limit, datatype=datatype)
1495
1496 def get_keras_embedding(self, train_embeddings=False):
~/env/lib64/python3.6/site-packages/gensim/models/utils_any2vec.py in _load_word2vec_format(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype)
383 with utils.ignore_deprecation_warning():
384 # TODO use frombuffer or something similar
--> 385 weights = fromstring(fin.read(binary_len), dtype=REAL).astype(datatype)
386 add_word(word, weights)
387 else:
/usr/lib64/python3.6/gzip.py in read(self, size)
274 import errno
275 raise OSError(errno.EBADF, "read() on write-only GzipFile object")
--> 276 return self._buffer.read(size)
277
278 def read1(self, size=-1):
/usr/lib64/python3.6/_compression.py in readinto(self, b)
66 def readinto(self, b):
67 with memoryview(b) as view, view.cast("B") as byte_view:
---> 68 data = self.read(len(byte_view))
69 byte_view[:len(data)] = data
70 return len(data)
/usr/lib64/python3.6/gzip.py in read(self, size)
480 break
481 if buf == b"":
--> 482 raise EOFError("Compressed file ended before the "
483 "end-of-stream marker was reached")
484
EOFError: Compressed file ended before the end-of-stream marker was reached
Any idea whats gone wrong/ how to overcome this issue?
Thanks in advance!

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