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from typing import Dict
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from transformers.pipelines.audio_utils import ffmpeg_read
#from datasets import load_dataset
import torch
SAMPLE_RATE = 16000
class EndpointHandler():
def __init__(self, path=""):
# load the model
#self.model = whisper.load_model("medium")
self.processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
self.model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2")
self.forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe")
def __call__(self, data: Dict[str, bytes]) -> Dict[str, str]:
"""
Args:
data (:obj:):
includes the deserialized audio file as bytes
Return:
A :obj:`dict`:. base64 encoded image
"""
# process input
inputs = data.pop("inputs", data)
audio_nparray = ffmpeg_read(inputs, SAMPLE_RATE)
audio_tensor= torch.from_numpy(audio_nparray)
#ds = load_dataset("common_voice", "fr", split="test", streaming=True)
#ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
#input_speech = next(iter(ds))["audio"]
#input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features
# run inference pipeline
result = self.model.transcribe(audio_nparray)
# postprocess the prediction
return {"text": result["text"]} |