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from transformers import WhisperProcessor, WhisperForConditionalGeneration
from transformers import Speech2TextForConditionalGeneration, Speech2TextProcessor
import plotly.graph_objs as go
from datasets import load_dataset
from datasets import Audio
import evaluate
import librosa
import numpy as np
import pandas as pd
N_SAMPLES = 30
wer_metric = evaluate.load("wer")
def run(data_subset:str, model_1:str, model_2:str, own_audio, own_transcription:str):
if data_subset is None:
raise ValueError("No Dataset selected")
if model_1 is None:
raise ValueError("No Model 1 selected")
if model_2 is None:
raise ValueError("No Model 2 selected")
if data_subset == "Common Voice":
dataset, text_column = load_Common_Voice()
elif data_subset == "VoxPopuli":
dataset, text_column = load_Vox_Populi()
elif data_subset == "OWN Recoding/Sample":
sr, audio = own_audio
audio = audio.astype(np.float32) / 32768.0
print("AUDIO: ", type(audio), audio)
audio = librosa.resample(audio, orig_sr=sr, target_sr=16000)
else:
# if data_subset is None then still load load_Common_Voice
dataset, text_column = load_Common_Voice()
print("Dataset Loaded")
model1, processor1 = load_model(model_1)
model2, processor2 = load_model(model_2)
print("Models Loaded")
if data_subset == "OWN Recoding/Sample":
sample = {"audio":{"array":audio,"sampling_rate":16000}}
transcription1 = model_compute(model1, processor1, sample, model_1)
transcription2 = model_compute(model2, processor2, sample, model_2)
transcriptions1 = [transcription1]
transcriptions2 = [transcription2]
references = [own_transcription]
wer1 = compute_wer(references, transcriptions1)
wer2 = compute_wer(references, transcriptions2)
results_md = f"""
#### {model_1}
- WER Score: {wer1}
#### {model_2}
- WER Score: {wer2}"""
# Create the bar plot
fig = go.Figure(
data=[
go.Bar(x=[f"{model_1}"], y=[wer1], showlegend=False),
go.Bar(x=[f"{model_2}"], y=[wer2], showlegend=False),
]
)
# Update the layout for better visualization
fig.update_layout(
title="Comparison of Two Models",
xaxis_title="Models",
yaxis_title="Value",
barmode="group",
)
df = pd.DataFrame({"references":references, "transcriptions 1":transcriptions1,"WER 1":[wer1],"transcriptions 2":transcriptions2,"WER 2":[wer2]})
yield results_md, fig, df
else:
references = []
transcriptions1 = []
transcriptions2 = []
WER1s = []
WER2s = []
counter = 0
for i, sample in enumerate(dataset, start=1):
print(counter)
counter += 1
references.append(sample[text_column])
if model_1 == model_2:
transcription = model_compute(model1, processor1, sample, model_1)
transcriptions1.append(transcription)
transcriptions2.append(transcription)
else:
transcription1 = model_compute(model1, processor1, sample, model_1)
transcription2 = model_compute(model2, processor2, sample, model_2)
transcriptions1.append(transcription1)
transcriptions2.append(transcription2)
WER1s.append(compute_wer([sample[text_column]], [transcription1]))
WER2s.append(compute_wer([sample[text_column]], [transcription2]))
results_md = f"""
{i}/{len(dataset)}-{'#'*i}{'_'*(N_SAMPLES-i)}
#### {model_1}
- WER Score: {sum(WER1s)/N_SAMPLES}
#### {model_2}
- WER Score: {sum(WER2s)/N_SAMPLES}"""
# Create the bar plot
fig = go.Figure(
data=[
go.Bar(x=[f"{model_1}"], y=[sum(WER1s)/N_SAMPLES], showlegend=False),
go.Bar(x=[f"{model_2}"], y=[sum(WER2s)/N_SAMPLES], showlegend=False),
]
)
# Update the layout for better visualization
fig.update_layout(
title="Comparison of Two Models",
xaxis_title="Models",
yaxis_title="Value",
barmode="group",
)
df = pd.DataFrame({"references":references, "transcriptions 1":transcriptions1,"WER 1":WER1s,"transcriptions 2":transcriptions2,"WER 2":WER2s})
yield results_md, fig, df
# DATASET LOADERS
def load_Common_Voice():
dataset = load_dataset("mozilla-foundation/common_voice_11_0", "en", revision="streaming", split="test", streaming=True, token=True, trust_remote_code=True)
text_column = "sentence"
dataset = dataset.take(N_SAMPLES)
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
dataset = list(dataset)
return dataset, text_column
def load_Vox_Populi():
# Load the dataset in streaming mode
dataset = load_dataset("facebook/voxpopuli", "en", split="test", streaming=True, trust_remote_code=True)
# Optionally, preview the first item to understand the structure (can be removed in production)
print(next(iter(dataset)))
# Take the first 120 examples to work with
dataset = dataset.take(N_SAMPLES+20)
text_column = "normalized_text"
# Filter out samples with empty or unwanted 'normalized_text' values and invalid audio
dataset = dataset.filter(lambda x: is_valid_sample(x[text_column], x['audio']))
# Take the first 100 examples after filtering
dataset = dataset.take(N_SAMPLES)
# Cast the 'audio' column to the desired sampling rate
dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
# Convert to list and return
dataset = list(dataset)
return dataset, text_column
def is_valid_sample(text, audio):
# Check if 'normalized_text' is valid
text = text.strip()
if text == "" or text == "ignore time segment in scoring":
return False
# Check if the 'audio' array is valid (not empty and meets length criteria)
if len(audio['array']) == 0: # Audio is empty
return False
# Optionally, check if the audio duration is within a certain range
duration = audio['array'].size / audio['sampling_rate']
if duration < 1.0 or duration > 60.0: # Example: Filter out audio shorter than 1 second or longer than 60 seconds
return False
return True
# MODEL LOADERS
def load_model(model_id:str):
if model_id == "openai/whisper-tiny.en":
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
elif model_id == "facebook/s2t-medium-librispeech-asr":
model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-medium-librispeech-asr")
processor = Speech2TextProcessor.from_pretrained("facebook/s2t-medium-librispeech-asr")
else:
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
return model, processor
# MODEL INFERENCE
def model_compute(model, processor, sample, model_id):
if model_id == "openai/whisper-tiny.en":
sample = sample["audio"]
input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
predicted_ids = model.generate(input_features)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
return transcription[0]
elif model_id == "facebook/s2t-medium-librispeech-asr":
sample = sample["audio"]
features = processor(sample["array"], sampling_rate=16000, padding=True, return_tensors="pt")
input_features = features.input_features
attention_mask = features.attention_mask
gen_tokens = model.generate(input_features=input_features, attention_mask=attention_mask)
transcription= processor.batch_decode(gen_tokens, skip_special_tokens=True)[0]
return transcription
else:
return model(sample)
# UTILS
def compute_wer(references, predictions):
wer = wer_metric.compute(references=references, predictions=predictions)
wer = round(N_SAMPLES * wer, 2)
return wer
# print(load_Vox_Populi())
# print(run("Common Voice", "openai/whisper-tiny.en", "openai/whisper-tiny.en", None, None)) |