问题描述
我是 R 的新手,并试图了解 Rshiny 以构建 UI。我正在尝试为我的 python 应用程序创建一个 UI,用于转录多个 wav 文件。下面有两个部分,第一个是我的 python 应用程序,第二个是我在 R 中使用 reticulate 来调用我的 transcribe.py 应用程序的闪亮应用程序。但由于某种原因,我没有收到任何输出。
我的 Python 应用程序运行良好,不需要代码审查。但是,Rshiny 应用程序没有正确执行 Python 应用程序以产生所需的结果。目的是让用户从 UI 转录文件并决定他们是否要下载 csv。
我有一个用于转录文件的 Python 应用程序,名为 transcribe.py-
import os
import json
import time
# import threading
from pathlib import Path
import concurrent.futures
# from os.path import join,dirname
from ibm_watson import SpeechToTextV1
from ibm_watson.websocket import RecognizeCallback,AudioSource
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import pandas as pd
# Replace with your api key.
my_api_key = "abc123"
# You can add a directory path to Path() if you want to run
# the project from a different folder at some point.
directory = Path().absolute()
authenticator = IAMAuthenticator(my_api_key)
service = SpeechToTextV1(authenticator=authenticator)
service.set_service_url('https://api.us-east.speech-to-text.watson.cloud.ibm.com')
# I used this URL.
# service.set_service_url('https://stream.watsonplatform.net/speech-to-text/api')
models = service.list_models().get_result()
#print(json.dumps(models,indent=2))
model = service.get_model('en-US_broadbandModel').get_result()
#print(json.dumps(model,indent=2))
# get data to a csv
########################RUN THIS PART SECOND#####################################
def process_data(json_data,output_path):
print(f"Processing: {output_path.stem}")
cols = ["transcript","confidence"]
dfdata = [[t[cols[0]],t[cols[1]]] for r in json_data.get('results') for t in r.get("alternatives")]
df0 = pd.DataFrame(data = dfdata,columns = cols)
df1 = pd.DataFrame(json_data.get("speaker_labels")).drop(["final","confidence"],axis=1)
# test3 = pd.concat([df0,df1],axis=1)
test3 = pd.merge(df0,df1,left_index = True,right_index = True)
# sentiment
print(f"Getting sentiment for: {output_path.stem}")
transcript = test3["transcript"]
transcript.dropna(inplace=True)
analyzer = SentimentIntensityAnalyzer()
text = transcript
scores = [analyzer.polarity_scores(txt) for txt in text]
# data = pd.DataFrame(text,columns = ["Text"])
data = transcript.to_frame(name="Text")
data2 = pd.DataFrame(scores)
# final_dataset= pd.concat([data,data2],axis=1)
final_dataset = pd.merge(data,data2,right_index = True)
# test4 = pd.concat([test3,final_dataset],axis=1)
test4 = pd.merge(test3,final_dataset,right_index = True)
test4.drop("Text",axis=1,inplace=True)
test4.rename(columns = {
"neg": "Negative","pos": "Positive","neu": "Neutral",},inplace=True)
# This is the name of the output csv file
test4.to_csv(output_path,index = False)
def process_audio_file(filename,output_type = "csv"):
audio_file_path = directory.joinpath(filename)
# Update output path to consider `output_type` parameter.
out_path = directory.joinpath(f"{audio_file_path.stem}.{output_type}")
print(f"Current file: '{filename}'")
with open(audio_file_path,"rb") as audio_file:
data = service.recognize(
audio = audio_file,speaker_labels = True,content_type = "audio/wav",inactivity_timeout = -1,model = "en-US_NarrowbandModel",continuous = True,).get_result()
print(f"Speech-to-text complete for: '{filename}'")
# Return data and output path as collection.
return [data,out_path]
def main():
print("Running main()...")
# Default num. workers == min(32,os.cpu_count() + 4)
n_workers = os.cpu_count() + 2
# Create generator for all .wav files in folder (and subfolders).
file_gen = directory.glob("**/*.wav")
with concurrent.futures.ThreadPoolExecutor(max_workers = n_workers) as executor:
futures = {executor.submit(process_audio_file,f) for f in file_gen}
for future in concurrent.futures.as_completed(futures):
pkg = future.result()
process_data(*pkg)
if __name__ == "__main__":
print(f"Program to process audio files has started.")
t_start = time.perf_counter()
main()
t_stop = time.perf_counter()
print(f"Done! Processing completed in {t_stop - t_start} seconds.")
在 Rstudio 中,我尝试过 -
R.UI 文件
library(shiny)
library(reticulate) # for reading Python code
library(dplyr)
library(stringr)
library(formattable) # for adding color to tables
library(shinybusy) # for busy bar
library(DT) # for dataTableOutput
use_python("/usr/lib/python3")
ui <- fluidPage(
add_busy_bar(color = "#5d98ff"),fileInput("wavFile","SELECT .WAV FILE",accept = ".wav"),uIoUtput("downloadData"),dataTableOutput("transcript"),)
R.Server 文件
server <- function(input,output) {
# .WAV File Selector ------------------------------------------------------
file <- reactive({
file <- input$wavFile # Get file from user input
gsub("\\\\","/",file$datapath) # Access the file path. Convert back slashes to forward slashes.
})
# Transcribe and Clean ----------------------------------------------------
transcript <- reactive({
req(input$wavFile) # Require a file before proceeding
source_python('transcribe.py') # Load the Python function # COMMENT LINE OUT WHEN TESTING NON-TRANSCRIPTION FUNCTIONALITY
transcript <- data.frame(transcribe(file())) # Transcribe the file # COMMENT LINE OUT WHEN TESTING NON-TRANSCRIPTION FUNCTIONALITY
# load('transcript.rdata') # Loads a dummy transcript # UNCOMMENT LINE OUT WHEN TESTING NON-TRANSCRIPTION FUNCTIONALITY
transcript$transcript <- unlist(transcript$transcript) # Transcript field comes in as a list. Unlist it.
transcript <- transcript[which(!(is.na(transcript$confidence))),] # Remove empty lines
names(transcript) <- str_to_title(names(transcript)) # Capitalize column headers
transcript # Return the transcript
})
# Use a server-side download button ---------------------------------------
# ...so that the download button only appears after transcription
output$downloadData <- renderUI({
req(transcript())
downloadButton("handleDownload","Download CSV")
})
output$handleDownload <- downloadHandler(
filename = function() {
paste('Transcript ',Sys.Date(),".csv",sep = "")
},content = function(file) {
write.csv(transcript(),file,row.names = FALSE)
}
)
# Transcript table --------------------------------------------------------
output$transcript <- renderDataTable({
as.datatable(formattable(
transcript() %>%
select(Transcript,Confidence,Negative,Positive
),list(Confidence = color_tile('#ffffff','#a2b3c8'),Negative = color_tile('#ffffff','#e74446'),Positive = color_tile('#ffffff',"#499650")
)
),rownames = FALSE,options =list(paging = FALSE)
)
})
# END ---------------------------------------------------------------------
}
解决方法
在闪亮中,您需要在 python 脚本中正确传递参数。一个简单的方法是在 python 脚本中定义一个函数,并以闪亮的方式调用该函数。
这是您修改后的python脚本(编辑了process_data函数并添加了run_script函数)-
import os
import json
import time
# import threading
from pathlib import Path
import concurrent.futures
# from os.path import join,dirname
from ibm_watson import SpeechToTextV1
from ibm_watson.websocket import RecognizeCallback,AudioSource
from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import pandas as pd
# Replace with your api key.
my_api_key = "api_key"
# You can add a directory path to Path() if you want to run
# the project from a different folder at some point.
directory = Path().absolute()
authenticator = IAMAuthenticator(my_api_key)
service = SpeechToTextV1(authenticator=authenticator)
service.set_service_url('https://api.us-east.speech-to-text.watson.cloud.ibm.com')
# I used this URL.
# service.set_service_url('https://stream.watsonplatform.net/speech-to-text/api')
models = service.list_models().get_result()
#print(json.dumps(models,indent=2))
model = service.get_model('en-US_BroadbandModel').get_result()
#print(json.dumps(model,indent=2))
# get data to a csv
########################RUN THIS PART SECOND#####################################
def process_data(json_data):
#print(f"Processing: {output_path.stem}")
cols = ["transcript","confidence"]
dfdata = [[t[cols[0]],t[cols[1]]] for r in json_data.get('results') for t in r.get("alternatives")]
df0 = pd.DataFrame(data = dfdata,columns = cols)
df1 = pd.DataFrame(json_data.get("speaker_labels")).drop(["final","confidence"],axis=1)
# test3 = pd.concat([df0,df1],axis=1)
test3 = pd.merge(df0,df1,left_index = True,right_index = True)
# sentiment
#print(f"Getting sentiment for: {output_path.stem}")
transcript = test3["transcript"]
transcript.dropna(inplace=True)
analyzer = SentimentIntensityAnalyzer()
text = transcript
scores = [analyzer.polarity_scores(txt) for txt in text]
# data = pd.DataFrame(text,columns = ["Text"])
data = transcript.to_frame(name="Text")
data2 = pd.DataFrame(scores)
# final_dataset= pd.concat([data,data2],axis=1)
final_dataset = pd.merge(data,data2,right_index = True)
# test4 = pd.concat([test3,final_dataset],axis=1)
test4 = pd.merge(test3,final_dataset,right_index = True)
test4.drop("Text",axis=1,inplace=True)
test4.rename(columns = {
"neg": "Negative","pos": "Positive","neu": "Neutral",},inplace=True)
# This is the name of the output csv file
# test4.to_csv(output_path,index = False)
return(test4)
def process_audio_file(filename,output_type = "csv"):
audio_file_path = directory.joinpath(filename)
# Update output path to consider `output_type` parameter.
out_path = directory.joinpath(f"{audio_file_path.stem}.{output_type}")
print(f"Current file: '{filename}'")
with open(audio_file_path,"rb") as audio_file:
data = service.recognize(
audio = audio_file,speaker_labels = True,content_type = "audio/wav",inactivity_timeout = -1,model = "en-US_NarrowbandModel",continuous = True,).get_result()
print(f"Speech-to-text complete for: '{filename}'")
# Return data and output path as collection.
return [data,out_path]
def main():
print("Running main()...")
# Default num. workers == min(32,os.cpu_count() + 4)
n_workers = os.cpu_count() + 2
# Create generator for all .wav files in folder (and subfolders).
file_gen = directory.glob("**/*.wav")
with concurrent.futures.ThreadPoolExecutor(max_workers = n_workers) as executor:
futures = {executor.submit(process_audio_file,f) for f in file_gen}
for future in concurrent.futures.as_completed(futures):
pkg = future.result()
process_data(*pkg)
def run_script (filename):
return(process_data(process_audio_file(filename)[0]))
闪亮代码
在服务器文件中调用 run_script 函数而不是转录。确保 transcribe.py 文件在工作目录中。更正了 output$transcript 中的一些错字
library(shiny)
library(reticulate) # for reading Python code
library(dplyr)
library(stringr)
library(formattable) # for adding color to tables
library(shinybusy) # for busy bar
library(DT) # for dataTableOutput
use_python("C:/Users/ap396/Anaconda3/python")
ui <- fluidPage(
add_busy_bar(color = "#5d98ff"),fileInput("wavFile","SELECT .WAV FILE",accept = ".wav",multiple = T),uiOutput("downloadData"),dataTableOutput("transcript")
)
server <- function(input,output) {
# .WAV File Selector ------------------------------------------------------
file <- reactive({
req(input$wavFile) # Require a file before proceeding
files <- input$wavFile # Get file from user input
file = NULL
for (i in 1:nrow(files)){
print(file)
file = c(file,gsub("\\\\","/",files$datapath[i])) # Access the file path. Convert back slashes to forward slashes.
}
return(file)
})
# Transcribe and Clean ----------------------------------------------------
source_python('transcribe.py')
transcript <- reactive({
dft= data.frame(NULL)
for(j in 1:length(file())){
t0 = Sys.time()
transcript <- run_script(file()[j]) # Transcribe the file # COMMENT LINE OUT WHEN TESTING NON-TRANSCRIPTION FUNCTIONALITY
t1 = Sys.time() - t0
transcript$File = j; transcript$Time = t1
dft = rbind(dft,transcript)
}
return(dft) # Return the transcript
})
# Use a server-side download button ---------------------------------------
# ...so that the download button only appears after transcription
output$downloadData <- renderUI({
req(transcript())
downloadButton("handleDownload","Download CSV")
})
output$handleDownload <- downloadHandler(
filename = function() {
paste('Transcript ',Sys.Date(),".csv",sep = "")
},content = function(file) {
write.csv(transcript(),file,row.names = FALSE)
}
)
# Transcript table --------------------------------------------------------
output$transcript <- renderDataTable({
as.datatable(formattable(
transcript() %>%
select(File,Time,transcript,confidence,Negative,Positive
),list(Confidence = color_tile('#ffffff','#a2b3c8'),Negative = color_tile('#ffffff','#e74446'),Positive = color_tile('#ffffff',"#499650")
)
),rownames = FALSE,options =list(paging = FALSE)
)
})
# END ---------------------------------------------------------------------
}
# Return a Shiny app object
shinyApp(ui = ui,server = server)