app.py

import openai, os
import gradio as gr
import time
import boto3
import json
import numpy as np
import wave
import io
import os
from langchain import OpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationSummaryBufferMemory
from langchain.chat_models import ChatOpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.schema import HumanMessage
import subprocess
from contextlib import closing
import asyncio

# This example uses aiofile for asynchronous file reads.
# It's not a dependency of the project but can be installed
# with `pip install aiofile`.
import aiofile

from amazon_transcribe.client import TranscribeStreamingClient
from amazon_transcribe.handlers import TranscriptResultStreamHandler
from amazon_transcribe.model import TranscriptEvent
from amazon_transcribe.utils import apply_realtime_delay

# Run a shell command
command = "which ffmpeg"
result = subprocess.run(command, shell=True, capture_output=True, text=True)

# Check the command output
if result.returncode == 0:
    print("Command executed successfully")
    print("Command output:")
    print(result.stdout)
else:
    print("Command failed")
    print("Error message:")
    print(result.stderr)


openai.api_key = os.environ["OPENAI_API_KEY"]

polly = boto3.client('polly', region_name='us-east-1')
s3 = boto3.client('s3')
transcribe = boto3.client('transcribe')


memory = ConversationSummaryBufferMemory(llm=ChatOpenAI(), max_token_limit=2048)
conversation = ConversationChain(
    llm=OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], max_tokens=2048, temperature=0.5), 
    memory=memory,
)

SAMPLE_RATE = 16000
BYTES_PER_SAMPLE = 2
CHANNEL_NUMS = 1
AUDIO_PATH = ''
CHUNK_SIZE = 1024 * 8
REGION = "us-west-2"
transcript_text = ''
transcriptions = []

class MyEventHandler(TranscriptResultStreamHandler):
    def __init__(self, transcript_result_stream):
            super().__init__(transcript_result_stream)
            self.transcriptions = []
    async def handle_transcript_event(self, transcript_event: TranscriptEvent):
        # This handler can be implemented to handle transcriptions as needed.
        # Here's an example to get started.
        results = transcript_event.transcript.results
        for result in results:
            for alt in result.alternatives:
                print(alt.transcript)
                transcriptions.append(alt.transcript)


async def basic_transcribe():
    # Setup up our client with our chosen AWS region
    client = TranscribeStreamingClient(region=REGION)

    # Start transcription to generate our async stream
    stream = await client.start_stream_transcription(
        language_code="zh-CN",
        media_sample_rate_hz=SAMPLE_RATE,
        media_encoding="pcm",
    )

    async def write_chunks():
        # NOTE: For pre-recorded files longer than 5 minutes, the sent audio
        # chunks should be rate limited to match the realtime bitrate of the
        # audio stream to avoid signing issues.
        async with aiofile.AIOFile(AUDIO_PATH, "rb") as afp:
            reader = aiofile.Reader(afp, chunk_size=CHUNK_SIZE)
            await apply_realtime_delay(
                stream, reader, BYTES_PER_SAMPLE, SAMPLE_RATE, CHANNEL_NUMS
            )
        await stream.input_stream.end_stream()

    # Instantiate our handler and start processing events
    handler = MyEventHandler(stream.output_stream)
    await asyncio.gather(write_chunks(), handler.handle_events())
    # Retrieve the transcriptions from the handler
    #transcriptions = handler.transcriptions


def download_file(bucket_name, object_key, file_path):
    try:
        # Download the file from S3
        s3.download_file(bucket_name, object_key, file_path)
        print(f"File downloaded successfully: {file_path}")
    except Exception as e:
        print(f"Error downloading file: {str(e)}")


def play_s3_voice(text):
    response = polly.start_speech_synthesis_task(
        OutputS3BucketName='lingo-audio-materials', #this bucket is in us-east-1
        OutputS3KeyPrefix='answers/',
        OutputFormat='mp3',
        Text=text,
        VoiceId='Zhiyu',
        LanguageCode='cmn-CN',
        Engine='neural'
    )
    
    # Print the task ID and status
    task_id = response['SynthesisTask']['TaskId']
    print('Task ID:', task_id)
    
    while True:
        task = polly.get_speech_synthesis_task(TaskId=task_id)
        task_status = task['SynthesisTask']['TaskStatus']
    
        if task_status == 'completed':
            break
        elif task_status  == 'failed':
            # Task failed
            print('Task failed:', task['SynthesisTask']['TaskStatusReason'])
            break
        else:
            print("Polly synthesis task is still in progress...")
            time.sleep(1)

    output_uri = response['SynthesisTask']['OutputUri']
    print("polly output_uri:"+output_uri)
    output_uri = output_uri.replace("https://", "")

    # Split the URI into bucket name and key
    results = output_uri.split("/")
    bucket_name = results[1]
    key_name = results[2]+'/'+results[3]
    print("bucket name:"+bucket_name)
    print("key name:"+key_name)

    mp3_pre_signed_url = s3.generate_presigned_url('get_object',Params={'Bucket': bucket_name,'Key': key_name},ExpiresIn=3600)
    print("mp3_pre_signed_url:"+mp3_pre_signed_url)

    output_file = "/tmp/from-s3.mp3"
    current_dir = os.getcwd()
    #file_absolute_path = current_dir+'/'+output_file
    print("current dir:"+current_dir)
    print("output_file_location: "+output_file)
    download_file(bucket_name, key_name, output_file)
    #encoded_path = file_absolute_path.encode("utf-8")

    #tmp_aud_file_url = output_file
    #htm_audio = f'<audio><source src={tmp_aud_file_url} type="audio/mp3" autoplay></audio>'
    #audio_htm = gr.HTML(htm_audio)
    return output_file 

def predict(input, history=[]):
    history.append(input)
    response = conversation.predict(input=input)
    print("GPT response: "+response)
    history.append(response)
    audio_file = play_s3_voice(response)
    responses = [(u,b) for u,b in zip(history[::2], history[1::2])]
    print("all historical responses: "+str(responses))
    return responses, audio_file, history

def transcribe_func_new(audio):
    audio_file = open(audio, "rb")
    wav_file = audio_file.name
    print("audio_file: "+wav_file)
    #transcript = openai.Audio.transcribe("whisper-1", audio_file)
    #return transcript['text']
    
    pcm_file = os.path.splitext(wav_file)[0] + ".pcm"

    wav_to_pcm(wav_file, pcm_file)
    AUDIO_PATH=pcm_file

    loop = asyncio.get_event_loop()
    loop.run_until_complete(basic_transcribe())
    loop.close()

    transcript_text = transcriptions[-1]
    print("final transcribe script: "+transcript_text)
    return transcript_text    

def transcribe_func_old(audio):
    audio_file = open(audio, "rb")
    file_name = audio_file.name
    #file_directory = os.path.dirname(audio_file.name)
    print("audio_file: "+file_name)
    #transcript = openai.Audio.transcribe("whisper-1", audio_file)
    #return transcript['text']
    
    # Set up the job parameters
    job_name = "lingo-demo"
    text_output_bucket = 'lingo-text-material' #this bucket is in us-west-1
    text_output_key = 'transcriptions/question.json'
    text_output_key = 'transcriptions/'+job_name+'.json'
    language_code = 'zh-CN'

    # Upload the file to an S3 bucket
    audio_input_bucket_name = "lingo-audio-material"
    audio_input_s3_key = "questions/tmp-question-from-huggingface.wav"
    
    s3.upload_file(file_name, audio_input_bucket_name, audio_input_s3_key)
    
    # Construct the S3 bucket URI
    s3_uri = f"s3://{audio_input_bucket_name}/{audio_input_s3_key}"

    response = transcribe.list_transcription_jobs()
    
    # Iterate through the jobs and print their names
    for job in response['TranscriptionJobSummaries']:
        print(job['TranscriptionJobName'])
        if job['TranscriptionJobName'] == job_name:
            response = transcribe.delete_transcription_job(TranscriptionJobName=job_name)
            print("delete transcribe job response:"+str(response))

    # Create the transcription job
    response = transcribe.start_transcription_job(
        TranscriptionJobName=job_name,
        Media={'MediaFileUri': s3_uri},
        MediaFormat='wav',
        LanguageCode=language_code,
        OutputBucketName=text_output_bucket,
        OutputKey=text_output_key
    )
    
    print("start transcribe job response:"+str(response))
    job_name = response["TranscriptionJob"]["TranscriptionJobName"]
    
    # Wait for the transcription job to complete
    while True:
        status = transcribe.get_transcription_job(TranscriptionJobName=job_name)['TranscriptionJob']['TranscriptionJobStatus']
        if status in ['COMPLETED', 'FAILED']:
            break
        print("Transcription job still in progress...")
        time.sleep(1)
    
    # Get the transcript
    #transcript = transcribe.get_transcription_job(TranscriptionJobName=job_name)
    transcript_uri = transcribe.get_transcription_job(TranscriptionJobName=job_name)['TranscriptionJob']['Transcript']['TranscriptFileUri']
    print("transcript uri: " + str(transcript_uri))
    
    transcript_file_content = s3.get_object(Bucket=text_output_bucket, Key=text_output_key)['Body'].read().decode('utf-8')
    print(transcript_file_content)
    json_data = json.loads(transcript_file_content)

    # Extract the transcript value
    transcript_text = json_data['results']['transcripts'][0]['transcript']
    return transcript_text    

def process_audio(audio, history=[]):
    text = transcribe_func(audio)
    return predict(text, history)

with gr.Blocks(css="#chatbot{height:350px} .overflow-y-auto{height:500px}") as demo:
    chatbot = gr.Chatbot(elem_id="chatbot")
    state = gr.State([])

    with gr.Row():
        txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
        
    with gr.Row():
        audio_input = gr.Audio(source="microphone", type="filepath",  label="Audio Input")

    with gr.Row():
        audio_output = gr.Audio(type="filepath", label="Audio Output", elem_id="speaker", interactive=False)

        #audio_html = gr.HTML()

    txt.submit(predict, [txt, state], [chatbot, audio_output, state])
    audio_input.change(process_audio, [audio_input, state], [chatbot, audio_output, state])

demo.launch(debug=True)