[Bug] Graph is finalized after using spleeter in python

## Description

I'm using `spleeter` as a python package in my code, and when using tensorflow as the `stft_backend`, the default global tensorflow graph will be finalized after spleeter finished the separation, leading to a situation that tensorflow models are unable to be initialized anymore in further process.

## Step to reproduce

1. Installed using `pip install spleeter`
2. Run as `spleeter` python pacakage
3. Got `RuntimeError: Graph is finalized and cannot be modified.` error

Version
- `spleeter` - 2.0.1
- `tensorflow` - 2.3.0

Steps to reproduce:
``` python
from spleeter.separator import Separator
import tensorflow as tf

sep = Separator("spleeter:2stems")
sep.separate_to_file("example.wav", "/tmp/test")

# Below will fail
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(10, 10)),
tf.keras.layers.Dense(10)
])
```

## Output

```bash
2020-11-30 23:16:38.229885: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-11-30 23:16:39.902822: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
2020-11-30 23:16:39.906834: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-30 23:16:39.907200: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1080 Ti computeCapability: 6.1
coreClock: 1.683GHz coreCount: 28 deviceMemorySize: 10.91GiB deviceMemoryBandwidth: 451.17GiB/s
2020-11-30 23:16:39.907235: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-11-30 23:16:39.909192: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2020-11-30 23:16:39.910422: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2020-11-30 23:16:39.910637: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2020-11-30 23:16:39.911894: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2020-11-30 23:16:39.912559: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2020-11-30 23:16:39.915218: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-11-30 23:16:39.915319: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-30 23:16:39.915741: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-30 23:16:39.916050: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-11-30 23:16:41.953061: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2020-11-30 23:16:41.973544: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 3600000000 Hz
2020-11-30 23:16:41.973886: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x68f98d0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-11-30 23:16:41.973915: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2020-11-30 23:16:42.031372: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-30 23:16:42.031785: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x4b76b40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-11-30 23:16:42.031803: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce GTX 1080 Ti, Compute Capability 6.1
2020-11-30 23:16:42.031954: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-30 23:16:42.032263: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1080 Ti computeCapability: 6.1
coreClock: 1.683GHz coreCount: 28 deviceMemorySize: 10.91GiB deviceMemoryBandwidth: 451.17GiB/s
2020-11-30 23:16:42.032291: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-11-30 23:16:42.032313: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2020-11-30 23:16:42.032327: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2020-11-30 23:16:42.032341: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2020-11-30 23:16:42.032354: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2020-11-30 23:16:42.032366: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2020-11-30 23:16:42.032379: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-11-30 23:16:42.032428: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-30 23:16:42.032766: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-30 23:16:42.033074: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-11-30 23:16:42.033095: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-11-30 23:16:42.333509: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-11-30 23:16:42.333558: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263] 0
2020-11-30 23:16:42.333566: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0: N
2020-11-30 23:16:42.333741: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-30 23:16:42.334136: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-11-30 23:16:42.334456: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7822 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
2020-11-30 23:16:43.200089: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2020-11-30 23:16:43.302117: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-11-30 23:16:43.893600: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
INFO:spleeter:File /tmp/test_spleeter/Ava/vocals.wav written succesfully
INFO:spleeter:File /tmp/test_spleeter/Ava/accompaniment.wav written succesfully
Traceback (most recent call last):
File "test.py", line 12, in
tf.keras.layers.Dense(10)
File "/data/omnizart/.venv/lib/python3.6/site-packages/tensorflow/python/training/tracking/base.py", line 457, in _method_wrapper
result = method(self, *args, **kwargs)
File "/data/omnizart/.venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py", line 142, in __init__
self.add(layer)
File "/data/omnizart/.venv/lib/python3.6/site-packages/tensorflow/python/training/tracking/base.py", line 457, in _method_wrapper
result = method(self, *args, **kwargs)
File "/data/omnizart/.venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py", line 202, in add
batch_shape=batch_shape, dtype=dtype, name=layer.name + '_input')
File "/data/omnizart/.venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/input_layer.py", line 311, in Input
input_layer = InputLayer(**input_layer_config)
File "/data/omnizart/.venv/lib/python3.6/site-packages/tensorflow/python/keras/engine/input_layer.py", line 160, in __init__
ragged=ragged)
File "/data/omnizart/.venv/lib/python3.6/site-packages/tensorflow/python/keras/backend.py", line 1223, in placeholder
x = array_ops.placeholder(dtype, shape=shape, name=name)
File "/data/omnizart/.venv/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 3100, in placeholder
return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
File "/data/omnizart/.venv/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py", line 6809, in placeholder
"Placeholder", dtype=dtype, shape=shape, name=name)
File "/data/omnizart/.venv/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 744, in _apply_op_helper
attrs=attr_protos, op_def=op_def)
File "/data/omnizart/.venv/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3460, in _create_op_internal
self._check_not_finalized()
File "/data/omnizart/.venv/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3050, in _check_not_finalized
raise RuntimeError("Graph is finalized and cannot be modified.")
RuntimeError: Graph is finalized and cannot be modified.

```

## Environment

| | |
| ----------------- | ------------------------------- |
| OS | Ubuntu-18.04 |
| Installation type | pip |
| RAM available | 128GB |
| Hardware spec | NVIDIA 1080TI 12GB / Intel Core I7-7700 / CUDA V11.0 / CUDA Driver: 450.80.0 |

## Additional context

Currently my workaround is to set `sep._params["stft_backend"] = "librosa"` to avoid using the tensorflow backend.
I've checked the code that in librosa approach, new local graph will be initialized, and later separation will all within that graph. The default global graph will thus not being finalized, and all would be fine when initializing new tensorflow models in later process.

I've also tried to initialize the tensorflow model after separation like following:
```python
with tf.Graph().as_default():
model = tf.keras.models.Sequential(...)
```
though it succeeded, but when I tried to load some pre-trained models inside the `with`, it was extremely slow, compared to load the model that is not in the graph mode. It usually takes only a few seconds to load the pre-trained models, but it takes up to 8~10 minutes to load the same model in graph mode.

Spleeter is a great tool and pretty easy to getting start that I've rarely found from other open sources. Hope this issue could help others when they enconter the same problem. It took me a few days getting around the bug, and hope there will be a good solution in the future.

Thanks.

Start at the spleeter.separator.Separator entry point and trace the TensorFlow stft_backend path used by separate_to_file; compare it with the librosa approach described in the issue. Done means the provided Python reproduction can create a subsequent tf.keras model without triggering “Graph is finalized and cannot be modified.”

Written by the indexing model from the issue text.