File size: 2,541 Bytes
e4f0af6 2302c8e e4f0af6 2302c8e e4f0af6 b18f6c8 e4f0af6 2302c8e e4f0af6 23e43c0 e4f0af6 b18f6c8 e4f0af6 b18f6c8 e4f0af6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
"""
This is just an example of what people would submit for
inference.
"""
from s3prl.downstream.runner import Runner
from typing import Dict
import torch
import os
class PreTrainedModel(Runner):
def __init__(self, path=""):
"""
Initialize downstream model.
"""
ckp_file = os.path.join(path, "hubert_asr.ckpt")
ckp = torch.load(ckp_file, map_location='cpu')
ckp["Args"].init_ckpt = ckp_file
ckp["Args"].mode = "inference"
ckp["Args"].device = "cpu" # Just to try in my computer
ckp["Config"]["downstream_expert"]["datarc"]["dict_path"]=os.path.join(path,'char.dict')
Runner.__init__(self, ckp["Args"], ckp["Config"])
def __call__(self, inputs)-> Dict[str, str]:
"""
Args:
inputs (:obj:`np.array`):
The raw waveform of audio received. By default at 16KHz.
Return:
A :obj:`dict`:. The object return should be liked {"text": "XXX"} containing
the detected text from the input audio.
"""
for entry in self.all_entries:
entry.model.eval()
inputs = [torch.FloatTensor(inputs)]
with torch.no_grad():
features = self.upstream.model(inputs)
features = self.featurizer.model(inputs, features)
preds = self.downstream.model.inference(features, [])
return {"text": preds[0]}
"""
import subprocess
import numpy as np
from datasets import load_dataset
# This is already done in the Inference API
def ffmpeg_read(bpayload: bytes, sampling_rate: int) -> np.array:
ar = f"{sampling_rate}"
ac = "1"
format_for_conversion = "f32le"
ffmpeg_command = [
"ffmpeg",
"-i",
"pipe:0",
"-ac",
ac,
"-ar",
ar,
"-f",
format_for_conversion,
"-hide_banner",
"-loglevel",
"quiet",
"pipe:1",
]
ffmpeg_process = subprocess.Popen(
ffmpeg_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE
)
output_stream = ffmpeg_process.communicate(bpayload)
out_bytes = output_stream[0]
audio = np.frombuffer(out_bytes, np.float32).copy()
if audio.shape[0] == 0:
raise ValueError("Malformed soundfile")
return audio
model = PreTrainedModel()
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
filename = ds[0]["file"]
with open(filename, "rb") as f:
data = ffmpeg_read(f.read(), 16000)
print(model(data))
""" |