Update ASR_1/src/asr_funs.py, ASR_1/src/main.py, ASR_1/src/run_funs.py,...

Update ASR_1/src/asr_funs.py, ASR_1/src/main.py, ASR_1/src/run_funs.py, ASR_1/src/trs_class.py, ASR_1/Dockerfile, ASR_1/README.md, ASR_1/requirements.txt, ASR_2/ASR.py, ASR_2/README.md, ASR_2/requirements.txt, README.md

ASR_1/README.md

+```
+cd $PATH_SHARED
+mkdir models
+cd models
+mkdir mmc_asr
+cd mmc_asr
+git lfs install
+git clone https://huggingface.co/openai/whisper-large-v3
+```

requirements.txt → ASR_1/requirements.txt

@@ -5,4 +5,4 @@ torchaudio # VAD needs it
 transformers
 typeguard==4.1.5
 typing_extensions==4.8.0
-whisper-timestamped
\ No newline at end of file
+whisper-timestamped

ASR_2/ASR.py

+import torch
+from transformers import pipeline
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+class AutomaticSpeechRecognition():
+    QuestionAudio = None
+    ##
+    QuestionText = None
+
+    def funcAutomaticSpeechRecognition(self, input):
+        '''
+        Verify the inference
+        '''
+        speech_reco = pipeline(
+            "automatic-speech-recognition", model="openai/whisper-base", device=device
+        )
+        res = speech_reco(input)
+        return res["text"]
+
+    def run(self):
+        self.QuestionText = self.funcAutomaticSpeechRecognition(self.QuestionAudio)
+
+if __name__ == '__main__':
+    module = AutomaticSpeechRecognition()
+    module.QuestionAudio = "path/to/audiofile"
+    module.run()
+    print(module.QuestionText)

ASR_2/README.md

+Implementation of MMC-ASR as a class in Python.
+
+## Installation
+Code was designed and tested on an Ubuntu 20.04 operating system using anaconda 23.7.2 and Python 3.9.
+An environment with all the necessary libraries can be created using:
+```bash
+conda create --name <env> --file requirements.txt
+```

ASR_2/requirements.txt

+torch
+transformers

README.md

-```
-cd $PATH_SHARED
-mkdir models
-cd models
-mkdir mmc_asr
-cd mmc_asr
-git lfs install
-git clone https://huggingface.co/openai/whisper-large-v3
-```
+# MPAI-MMC Automatic Speech Recognition
+
+
+This code refers to the implementation of the MPAI-NNW under MPAI-AIF, as described in the [AIMs](https://mpai.community/standards/mpai-mmc/v2-2/ai-modules/automatic-speech-recognition/.
+
+### Guide to the ASR code #1
+
+The code takes Speech Objects from MMC-AUS and generates Text Segments (called text transcripts). It uses the whisper-large-v3 model to convert an input Speech Object (speaker’s turn) into a Text Segment (here called text transcript). Disfluencies (e.g., repetitions, repairs, filled pauses) are often omitted. The Whisper reference document is available.
+
+The MMC-ASR Reference Software is found at the MPAI gitlab site. Use of this AI Modules is for developers who are familiar with Python, Docker, RabbitMQ, and downloading models from HuggingFace. The Reference Software contains:
+
+    1. src: a folder with the Python code implementing the AIM
+    2. Dockerfile: a Docker file containing only the libraries required to build the Docker image and run the container
+    3. requirements.txt: dependencies installed in the Docker image
+    4. README.md: commands for cloning https://huggingface.co/openai/whisper-large-v3
+
+Library: https://github.com/linto-ai/whisper-timestamped
+
+### Guide to the ASR code #2
+
+Use of this AI Modules is for developers who are familiar with Python and downloading models from HuggingFace,
+
+A wrapper for the Whisper NN Module:
+
+    1. Manages input files and parameters: Speech Object
+    2. Performs Speech Recognition on each Speech Object by executing the Whisper Module.
+    3. Outputs Recognised Text.
+
+The MMC-ASR Reference Software is found at the NNW gitlab  site (registration required). It contains:
+
+    1. The python code implementing the AIM.
+    2. The required libraries are: pytorch and transformers (HuggingFace).
+