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import os
import sys
os.system("pip install transformers==4.27.0")
os.system("pip install numpy==1.23")
from transformers import pipeline, WhisperModel, WhisperTokenizer, WhisperFeatureExtractor, AutoFeatureExtractor, AutoProcessor, WhisperConfig
os.system("pip install jiwer")
from jiwer import wer
os.system("pip install datasets[audio]")
from evaluate import evaluator
import evaluate
from datasets import load_dataset, Audio, disable_caching, set_caching_enabled
import gradio as gr
set_caching_enabled(False)
disable_caching()
huggingface_token = os.environ["huggingface_token"]
pipe = pipeline(model="mskov/whisper-small-esc50")
print(pipe)
dataset = load_dataset("mskov/miso_test", split="test").cast_column("audio", Audio(sampling_rate=16000))
print(dataset, "and at 0[audio][array] ", dataset[0]["audio"]["array"], type(dataset[0]["audio"]["array"]), "and at audio : ", dataset[0]["audio"])
def evalWhisper(model, dataset):
model.eval()
print("model.eval ", model.eval())
# Evaluate the model
model.eval()
print("model.eval ", model.eval())
with torch.no_grad():
outputs = model(input_ids=input_ids, attention_mask=attention_mask)
print("outputs ", outputs)
# Convert predicted token IDs back to text
predicted_text = tokenizer.batch_decode(outputs.logits.argmax(dim=-1), skip_special_tokens=True)
# Get ground truth labels from the dataset
labels = dataset["audio"] # Replace "labels" with the appropriate key in your dataset
print("labels are ", labels)
# Compute WER
wer_score = wer(labels, predicted_text)
# Print or return WER score
print(f"Word Error Rate (WER): {wer_score}")
def transcribe(audio):
text = pipe(audio)["text"]
test = evalWhisper(model, dataset)
return text, test
iface = gr.Interface(
fn=transcribe,
inputs=gr.Audio(source="microphone", type="filepath"),
outputs="text",
title="Whisper Small Miso Test",
)
iface.launch()
'''
print("check check")
print(inputs)
input_features = inputs.input_features
decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
list(last_hidden_state.shape)
print(list(last_hidden_state.shape))
''' |