zkmkarlsruhe / language-identification Goto Github PK
View Code? Open in Web Editor NEWSpoken Language Identification on Common Voice and AudioSet using Deep Learning
License: Other
Spoken Language Identification on Common Voice and AudioSet using Deep Learning
License: Other
Hi,
At the first, thanks for the valuable repo.
I have some audio file with average length of 15 minutes that several people with different language are talking in it.
How can I use your pretrained model to handle the aforementioned audio file?
Best regards
@bytosaur
@danomatika
@loelkes
Hi! Can you add detailed steps on how to train your model using a custom dataset?
Hi! I am having this issue when training with batch size > 1:
tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: Cannot add tensor to the batch: number of elements does not match. Shapes are: [tensor]: [119130,1], [batch]: [80000,1]
[[node IteratorGetNext (defined at train.py:117) ]]
(1) Invalid argument: Cannot add tensor to the batch: number of elements does not match. Shapes are: [tensor]: [119130,1], [batch]: [80000,1]
[[node IteratorGetNext (defined at train.py:117) ]]
[[Shape/_4]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_14011]
Function call stack:
train_function -> train_function
Hello , i have an issue with the Google Colab file. Here is my error with the last cell execution:
ValueError Traceback (most recent call last)
in ()
----> 1 prediction = model.predict(audio)
9 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
992 # invariant: func_outputs
contains only Tensors, CompositeTensors,
993 # TensorArrays and None
s.
--> 994 func_outputs = nest.map_structure(convert, func_outputs,
995 expand_composites=True)
996
ValueError: in user code:
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1586 predict_function *
return step_function(self, iterator)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1576 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1286 run
# /job:localhost/replica:0/task:0/device:GPU:0
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2849 call_for_each_replica
`ReplicaContext`, which can only be called inside the function passed to
/usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3632 _call_for_each_replica
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1569 run_step **
outputs = model.predict_step(data)
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1537 predict_step
return self(x, training=False)
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:1037 __call__
outputs = call_fn(inputs, *args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:415 call
inputs, training=training, mask=mask)
/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:550 _run_internal_graph
outputs = node.layer(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:1037 __call__
outputs = call_fn(inputs, *args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:369 call
return super(Sequential, self).call(inputs, training=training, mask=mask)
/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:415 call
inputs, training=training, mask=mask)
/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:550 _run_internal_graph
outputs = node.layer(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:1037 __call__
outputs = call_fn(inputs, *args, **kwargs)
/usr/local/lib/python3.7/dist-packages/keras/saving/saved_model/utils.py:68 return_outputs_and_add_losses
outputs, losses = fn(*args, **kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py:885 __call__
else:
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py:924 _call
"\n"
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py:3038 __call__
seen_names.add(proposal)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py:3463 _maybe_define_function
@tf.contrib.eager.defun
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py:3308 _create_graph_function
TypeError: If the function inputs include non-hashable objects
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py:1007 func_graph_from_py_func
for arg in (nest.flatten(func_args, expand_composites=True) +
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py:668 wrapped_fn
/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/function_deserialization.py:294 restored_function_body
def load_function_def_library(library, load_shared_name_suffix=None):
ValueError: Could not find matching function to call loaded from the SavedModel. Got:
Positional arguments (1 total):
* Tensor("x:0", shape=(None, 80000, 2), dtype=float32)
Keyword arguments: {}
Expected these arguments to match one of the following 1 option(s):
Option 1:
Positional arguments (1 total):
* TensorSpec(shape=(None, 80000, 1), dtype=tf.float32, name='x')
Keyword arguments: {}
First of all, thank you for sharing your models!
I was wondering if you trim the audio from silince during preprocessing, because your model works pretty well where there's voice right away but if someone was lingering in the beginning of the audio, your model predicts noise. How do you think one should approach the issue?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.