Add model

README.md

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+---
+tags:
+- asteroid
+- audio
+- ConvTasNet
+- audio-to-audio
+datasets:
+- Libri1Mix
+- enh_single
+license: cc-by-sa-3.0
+---
+
+## Asteroid model `JorisCos/ConvTasNet_Libri1Mix_enhsignle_16k`
+
+Description:
+
+This model was trained by Joris Cosentino using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
+It was trained on the `enh_single` task of the Libri1Mix  dataset.
+
+Training config:
+
+```yml
+data:
+  n_src: 1
+  sample_rate: 16000
+  segment: 3
+  task: enh_single
+  train_dir: data/wav16k/min/train-360
+  valid_dir: data/wav16k/min/dev
+filterbank:
+  kernel_size: 32
+  n_filters: 512
+  stride: 16
+masknet:
+  bn_chan: 128
+  hid_chan: 512
+  mask_act: relu
+  n_blocks: 8
+  n_repeats: 3
+  n_src: 1
+  skip_chan: 128
+optim:
+  lr: 0.001
+  optimizer: adam
+  weight_decay: 0.0
+training:
+  batch_size: 6
+  early_stop: true
+  epochs: 200
+  half_lr: true
+  num_workers: 4
+
+```
+  
+
+Results:
+
+On Libri1Mix min test set :
+```yml
+si_sdr: 14.743051006476085
+si_sdr_imp: 11.293269700616385
+sdr: 15.300522933671061
+sdr_imp: 11.797860134458015
+sir: Infinity
+sir_imp: NaN
+sar: 15.300522933671061
+sar_imp: 11.797860134458015
+stoi: 0.9310514162434267
+stoi_imp: 0.13513159270288563
+```
+
+
+License notice:
+
+This work "ConvTasNet_Libri1Mix_enhsignle_16k" is a derivative of [LibriSpeech ASR corpus](http://www.openslr.org/12) by Vassil Panayotov,
+used under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/); of The WSJ0 Hipster Ambient Mixtures 
+dataset by [Whisper.ai](http://wham.whisper.ai/), used under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (Research only). 
+"ConvTasNet_Libri1Mix_enhsignle_16k" is licensed under [Attribution-ShareAlike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/) by Joris Cosentino

pipeline.py

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+from typing import Dict, List, Union
+from PIL import Image
+
+import os
+import json
+import numpy as np
+
+from fastai.learner import load_learner
+
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        # IMPLEMENT_THIS
+        # Preload all the elements you are going to need at inference.
+        # For instance your model, processors, tokenizer that might be needed.
+        # This function is only called once, so do all the heavy processing I/O here"""
+        self.model = BaseModel.from_pretrained("")
+        self.sampling_rate = self.model.sample_rate
+
+     def __call__(self, inputs: np.array) -> Tuple[np.array, int, List[str]]:
+        """
+        Args:
+            inputs (:obj:`np.array`):
+                The raw waveform of audio received. By default sampled at `self.sampling_rate`.
+                The shape of this array is `T`, where `T` is the time axis
+        Return:
+            A :obj:`tuple` containing:
+              - :obj:`np.array`:
+                 The return shape of the array must be `C'`x`T'`
+              - a :obj:`int`: the sampling rate as an int in Hz.
+              - a :obj:`List[str]`: the annotation for each out channel.
+                    This can be the name of the instruments for audio source separation
+                    or some annotation for speech enhancement. The length must be `C'`.
+        """
+        separated = separate.numpy_separate(self.model, inputs.reshape((1, 1, -1)))
+        # FIXME: how to deal with multiple sources?
+        out = separated[0]
+        n = out.shape[0]
+        labels = [f"label_{i}" for i in range(n)]
+        return separated[0], int(self.model.sample_rate), labels

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dd8ddefe95a35761f8a48643a618eba908572d04d33208a8ed5451fb5a4378d0
+size 20130704