def add_transform_output(client,
account_name,
resource_group_name,
transform_name,
preset,
insights_to_extract=None,
video_analysis_mode=None,
audio_language=None,
audio_analysis_mode=None,
on_error=None,
relative_priority=None,
resolution=None,
face_detector_mode=None,
blur_type=None):
transform = client.get(resource_group_name, account_name, transform_name)
if not transform:
show_resource_not_found_message(resource_group_name, account_name,
'transforms', transform_name)
transform.outputs.append(
build_transform_output(preset, insights_to_extract,
video_analysis_mode, audio_language,
audio_analysis_mode, on_error,
relative_priority, resolution,
face_detector_mode, blur_type))
parameters = Transform(outputs=transform.outputs)
return client.create_or_update(resource_group_name, account_name,
transform_name, parameters)
def create_transform(client,
account_name,
resource_group_name,
transform_name,
preset,
insights_to_extract=None,
video_analysis_mode=None,
audio_language=None,
audio_analysis_mode=None,
on_error=None,
relative_priority=None,
description=None,
resolution=None,
face_detector_mode=None,
blur_type=None):
outputs = [
build_transform_output(preset, insights_to_extract,
video_analysis_mode, audio_language,
audio_analysis_mode, on_error,
relative_priority, resolution,
face_detector_mode, blur_type)
]
parameters = Transform(description=description, outputs=outputs)
return client.create_or_update(resource_group_name, account_name,
transform_name, parameters)
def remove_transform_output(client, account_name, resource_group_name, transform_name, output_index):
transform = client.get(resource_group_name, account_name, transform_name)
try:
transform.outputs.pop(output_index)
except IndexError:
raise CLIError("index {} doesn't exist on outputs".format(output_index))
parameters = Transform(outputs=transform.outputs)
return client.create_or_update(resource_group_name, account_name, transform_name, parameters)
audio_language=
"en-US", # Be sure to modify this to your desired language code in BCP-47 format
insights_to_extract=
"AllInsights", # Video Analyzer can also run in Video only mode.
mode=
"Standard", # Video analyzer can also process audio in basic or standard mode when using All Insights
experimental_options=
{ # Optional settings for preview or experimental features
# "SpeechProfanityFilterMode="None" " # Disables the speech-to-text profanity filtering
}))
# Ensure that you have customized transforms for the AudioAnalyzer. This is really a one time setup operation.
print("Creating Audio Analyzer transform...")
# Adding transform details
my_transform = Transform()
my_transform.description = "A simple Audio Analyzer Transform"
my_transform.outputs = [audio_transform_output]
print(f"Creating transform {audio_transform_name}")
transform = client.transforms.create_or_update(
resource_group_name=resource_group,
account_name=account_name,
transform_name=audio_transform_name,
parameters=my_transform)
print(f"{audio_transform_name} created (or updated if it existed already). ")
# Ensure that you have customized transforms for the VideoAnalyzer. This is really a one time setup operation.
print("Creating Video Analyzer transform...")
transform_name = 'ContentAwareEncodingStreamFilesSample'
# Create a new Standard encoding Transform for Built-in Copy Codec
print(f"Creating Encoding transform named: {transform_name}")
# For this snippet, we are using 'BuiltInStandardEncoderPreset'
transform_output = TransformOutput(
preset=BuiltInStandardEncoderPreset(preset_name="ContentAwareEncoding"),
# What should we do with the job if there is an error?
on_error=OnErrorType.STOP_PROCESSING_JOB,
# What is the relative priority of this job to others? Normal, high or low?
relative_priority=Priority.NORMAL)
print("Creating encoding transform...")
# Adding transform details
my_transform = Transform()
my_transform.description = "Transform with Stream Files"
my_transform.outputs = [transform_output]
print(f"Creating transform {transform_name}")
transform = client.transforms.create_or_update(
resource_group_name=resource_group,
account_name=account_name,
transform_name=transform_name,
parameters=my_transform)
print(f"{transform_name} created (or updated if it existed already). ")
job_name = 'StreamFilesSample' + uniqueness
print(f"Creating custom encoding job {job_name}")
files = (source_file)
def transform_update_setter(client, resource_group_name, account_name,
transform_name, parameters):
parameters = Transform(outputs=parameters.outputs,
description=parameters.description)
return client.create_or_update(resource_group_name, account_name,
transform_name, parameters)
print(f"Creating Encoding transform named: {transform_name}")
# For this snippet, we are using 'BuiltInStandardEncoderPreset'
transform_output = TransformOutput(
preset=BuiltInStandardEncoderPreset(
preset_name="ContentAwareEncoding"
),
# What should we do with the job if there is an error?
on_error=OnErrorType.STOP_PROCESSING_JOB,
# What is the relative priority of this job to others? Normal, high or low?
relative_priority=Priority.NORMAL
)
print("Creating encoding transform...")
# Adding transform details
my_transform = Transform()
my_transform.description="Transform with Asset filters"
my_transform.outputs = [transform_output]
print(f"Creating transform {transform_name}")
transform = client.transforms.create_or_update(
resource_group_name=resource_group,
account_name=account_name,
transform_name=transform_name,
parameters=my_transform)
print(f"{transform_name} created (or updated if it existed already). ")
job_name = 'ContentAwareEncodingAssetFilters'+ uniqueness
print(f"Creating custom encoding job {job_name}")
files = (source_file)
transform_name = 'ContentAwareEncoding'
# Create a Encoding transform
print(f"Creating Encoding transform named: {transform_name}")
# For this snippet, we are using 'BuiltInStandardEncoderPreset'
transform_output = TransformOutput(
preset=BuiltInStandardEncoderPreset(preset_name="ContentAwareEncoding"),
# What should we do with the job if there is an error?
on_error=OnErrorType.STOP_PROCESSING_JOB,
# What is the relative priority of this job to others? Normal, high or low?
relative_priority=Priority.NORMAL)
print("Creating encoding transform...")
# Adding transform details
my_transform = Transform()
my_transform.description = "Transform with List Tracks in Assets"
my_transform.outputs = [transform_output]
print(f"Creating transform {transform_name}")
transform = client.transforms.create_or_update(
resource_group_name=resource_group,
account_name=account_name,
transform_name=transform_name,
parameters=my_transform)
print(f"{transform_name} created (or updated if it existed already). ")
job_name = 'ListTracksInAsset' + uniqueness
print(f"Creating custom encoding job {job_name}")
files = (source_file)
# Adjust the path as needed depending on how you are launching this python sample file.
# Upload the video to storage as a block blob
with open(upload_file_path, "rb") as data:
# From SDK
# upload_blob(data, blob_type=<BlobType.BlockBlob: 'BlockBlob'>, length=None, metadata=None, **kwargs)
blob_client.upload_blob(data)
### Create a Transform ###
transform_name = 'MyTrans' + str(uniqueness)
# From SDK
# TransformOutput(*, preset, on_error=None, relative_priority=None, **kwargs) -> None
transform_output = TransformOutput(preset=BuiltInStandardEncoderPreset(
preset_name="AdaptiveStreaming"))
transform = Transform()
transform.outputs = [transform_output]
print("Creating transform " + transform_name)
# From SDK
# Create_or_update(resource_group_name, account_name, transform_name, outputs, description=None, custom_headers=None, raw=False, **operation_config)
transform = client.transforms.create_or_update(
resource_group_name=os.getenv("RESOURCEGROUP"),
account_name=os.getenv("ACCOUNTNAME"),
transform_name=transform_name,
parameters=transform)
### Create a Job ###
job_name = 'MyJob' + str(uniqueness)
print("Creating job " + job_name)
files = (source_file)
# The output of this mode includes an Insights JSON file including only the keywords, transcription, and timing information.
# Automatic language detection and speaker diarization are not included in this mode.
# Standard : Performs all operations included in the Basic mode, additionally performing language detection and speaker diarization.
mode=AudioAnalysisMode.
BASIC # Change this to Standard if you would like to use the more advanced audio analyzer
),
# What should we do with the job if there is an error?
on_error=OnErrorType.STOP_PROCESSING_JOB,
# What is the relative priority of this job to others? Normal, high or low?
relative_priority=Priority.NORMAL)
# Ensure that you have customized transforms for the AudioAnalyzer. This is really a one time setup operation.
print("Creating Audio Analyzer transforms...")
# Adding transform details
my_transform = Transform()
my_transform.description = "A simple Audio Analyzer Transform"
my_transform.outputs = [transform_output]
print(f"Creating transform {transform_name}")
transform = client.transforms.create_or_update(
resource_group_name=resource_group,
account_name=account_name,
transform_name=transform_name,
parameters=my_transform)
print(f"{transform_name} created (or updated if it existed already). ")
job_name = 'AudioAnalyticsJob' + uniqueness
print(f"Creating AudioAnalytics job {job_name}")
files = (source_file)