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""" |
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This is just an example of what people would submit for |
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inference. |
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""" |
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|
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from s3prl.downstream.runner import Runner |
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from typing import Dict |
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import torch |
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import os |
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|
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class PreTrainedModel(Runner): |
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def __init__(self, path=""): |
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""" |
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Initialize downstream model. |
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""" |
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ckp_file = os.path.join(path, "hubert_asr.ckpt") |
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ckp = torch.load(ckp_file, map_location='cpu') |
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ckp["Args"].init_ckpt = ckp_file |
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ckp["Args"].mode = "inference" |
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ckp["Args"].upstream = "osanseviero/hubert_base" |
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ckp["Args"].upstream_model_name = "model.pt" |
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ckp["Args"].from_hf_hub = True |
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ckp["Args"].device = "cpu" |
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ckp["Config"]["downstream_expert"]["datarc"]["dict_path"]=os.path.join(path,'char.dict') |
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Runner.__init__(self, ckp["Args"], ckp["Config"]) |
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|
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def __call__(self, inputs)-> Dict[str, str]: |
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""" |
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Args: |
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inputs (:obj:`np.array`): |
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The raw waveform of audio received. By default at 16KHz. |
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Return: |
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A :obj:`dict`:. The object return should be liked {"text": "XXX"} containing |
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the detected text from the input audio. |
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""" |
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for entry in self.all_entries: |
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entry.model.eval() |
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inputs = [torch.FloatTensor(inputs)] |
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with torch.no_grad(): |
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features = self.upstream.model(inputs) |
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features = self.featurizer.model(inputs, features) |
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preds = self.downstream.model.inference(features, []) |
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return {"text": preds[0]} |
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""" |
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import subprocess |
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import numpy as np |
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from datasets import load_dataset |
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# This is already done in the Inference API |
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def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array: |
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ar = f"{sampling_rate}" |
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ac = "1" |
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format_for_conversion = "f32le" |
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ffmpeg_command = [ |
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"ffmpeg", |
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"-i", |
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"pipe:0", |
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"-ac", |
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ac, |
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"-ar", |
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ar, |
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"-f", |
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format_for_conversion, |
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"-hide_banner", |
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"-loglevel", |
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"quiet", |
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"pipe:1", |
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] |
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ffmpeg_process = subprocess.Popen( |
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ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE |
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) |
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output_stream = ffmpeg_process.communicate(bpayload) |
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out_bytes = output_stream[0] |
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audio = np.frombuffer(out_bytes, np.float32).copy() |
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if audio.shape[0] == 0: |
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raise ValueError("Malformed soundfile") |
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return audio |
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|
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model = PreTrainedModel() |
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ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") |
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filename = ds[0]["file"] |
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with open(filename, "rb") as f: |
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data = ffmpeg_read(f.read(), 16000) |
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print(model(data)) |
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""" |
