ragflow/rag/llm/sequence2txt_model.py

#
#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
#
import requests
from openai.lib.azure import AzureOpenAI
from zhipuai import ZhipuAI
import io
from abc import ABC
from ollama import Client
from openai import OpenAI
import os
import json
from rag.utils import num_tokens_from_string
import base64
import re


class Base(ABC):
    def __init__(self, key, model_name):
        pass

    def transcription(self, audio, **kwargs):
        transcription = self.client.audio.transcriptions.create(
            model=self.model_name,
            file=audio,
            response_format="text"
        )
        return transcription.text.strip(), num_tokens_from_string(transcription.text.strip())

    def audio2base64(self, audio):
        if isinstance(audio, bytes):
            return base64.b64encode(audio).decode("utf-8")
        if isinstance(audio, io.BytesIO):
            return base64.b64encode(audio.getvalue()).decode("utf-8")
        raise TypeError("The input audio file should be in binary format.")


class GPTSeq2txt(Base):
    def __init__(self, key, model_name="whisper-1", base_url="https://api.openai.com/v1"):
        if not base_url: base_url = "https://api.openai.com/v1"
        self.client = OpenAI(api_key=key, base_url=base_url)
        self.model_name = model_name


class QWenSeq2txt(Base):
    def __init__(self, key, model_name="paraformer-realtime-8k-v1", **kwargs):
        import dashscope
        dashscope.api_key = key
        self.model_name = model_name

    def transcription(self, audio, format):
        from http import HTTPStatus
        from dashscope.audio.asr import Recognition

        recognition = Recognition(model=self.model_name,
                                  format=format,
                                  sample_rate=16000,
                                  callback=None)
        result = recognition.call(audio)

        ans = ""
        if result.status_code == HTTPStatus.OK:
            for sentence in result.get_sentence():
                ans += sentence.text.decode('utf-8') + '\n'
            return ans, num_tokens_from_string(ans)

        return "**ERROR**: " + result.message, 0


class AzureSeq2txt(Base):
    def __init__(self, key, model_name, lang="Chinese", **kwargs):
        self.client = AzureOpenAI(api_key=key, azure_endpoint=kwargs["base_url"], api_version="2024-02-01")
        self.model_name = model_name
        self.lang = lang


class XinferenceSeq2txt(Base):
    def __init__(self,key,model_name="whisper-small",**kwargs):
        self.base_url = kwargs.get('base_url', None)
        self.model_name = model_name
        self.key = key

    def transcription(self, audio, language="zh", prompt=None, response_format="json", temperature=0.7):
        if isinstance(audio, str):
            audio_file = open(audio, 'rb')
            audio_data = audio_file.read()
            audio_file_name = audio.split("/")[-1]
        else:
            audio_data = audio
            audio_file_name = "audio.wav"

        payload = {
            "model": self.model_name,
            "language": language,
            "prompt": prompt,
            "response_format": response_format,
            "temperature": temperature
        }

        files = {
            "file": (audio_file_name, audio_data, 'audio/wav')
        }

        try:
            response = requests.post(
                f"{self.base_url}/v1/audio/transcriptions",
                files=files,
                data=payload
            )
            response.raise_for_status()
            result = response.json()

            if 'text' in result:
                transcription_text = result['text'].strip()
                return transcription_text, num_tokens_from_string(transcription_text)
            else:
                return "**ERROR**: Failed to retrieve transcription.", 0

        except requests.exceptions.RequestException as e:
            return f"**ERROR**: {str(e)}", 0


class TencentCloudSeq2txt(Base):
    def __init__(
            self, key, model_name="16k_zh", base_url="https://asr.tencentcloudapi.com"
    ):
        from tencentcloud.common import credential
        from tencentcloud.asr.v20190614 import asr_client

        key = json.loads(key)
        sid = key.get("tencent_cloud_sid", "")
        sk = key.get("tencent_cloud_sk", "")
        cred = credential.Credential(sid, sk)
        self.client = asr_client.AsrClient(cred, "")
        self.model_name = model_name

    def transcription(self, audio, max_retries=60, retry_interval=5):
        from tencentcloud.common.exception.tencent_cloud_sdk_exception import (
            TencentCloudSDKException,
        )
        from tencentcloud.asr.v20190614 import models
        import time

        b64 = self.audio2base64(audio)
        try:
            # dispatch disk
            req = models.CreateRecTaskRequest()
            params = {
                "EngineModelType": self.model_name,
                "ChannelNum": 1,
                "ResTextFormat": 0,
                "SourceType": 1,
                "Data": b64,
            }
            req.from_json_string(json.dumps(params))
            resp = self.client.CreateRecTask(req)

            # loop query
            req = models.DescribeTaskStatusRequest()
            params = {"TaskId": resp.Data.TaskId}
            req.from_json_string(json.dumps(params))
            retries = 0
            while retries < max_retries:
                resp = self.client.DescribeTaskStatus(req)
                if resp.Data.StatusStr == "success":
                    text = re.sub(
                        r"\[\d+:\d+\.\d+,\d+:\d+\.\d+\]\s*", "", resp.Data.Result
                    ).strip()
                    return text, num_tokens_from_string(text)
                elif resp.Data.StatusStr == "failed":
                    return (
                        "**ERROR**: Failed to retrieve speech recognition results.",
                        0,
                    )
                else:
                    time.sleep(retry_interval)
                    retries += 1
            return "**ERROR**: Max retries exceeded. Task may still be processing.", 0

        except TencentCloudSDKException as e:
            return "**ERROR**: " + str(e), 0
        except Exception as e:
            return "**ERROR**: " + str(e), 0