Youtube Video Summarisation using Mistral 7B, LangChain, and Whisper

pip install wandb

wandb login

table = wandb.Table(data=df)
run.log({'data':table})

learn = Learner(dls, 
                model,
                opt_func=ranger,
                loss_func=CrossEntropyLossFlat(),
                cbs=WandbCallback(),
                splitter=partial(blurr_seq2seq_splitter, arch=hf_arch)).to_fp16()

import whisper

model = whisper.load_model("base")
result = model.transcribe("example.mp4")

transcribed_text = result['text']

# Save the transcribed text to a file
with open("text.txt", "w") as file:
    file.write(transcribed_text)

print("Transcribed text saved to text.txt")

def summarize_transcript(filename):
    # Load transcript
    loader = TextLoader(filename)
    docs = loader.load()

    # Load LLM
    config = {'max_new_tokens': 4096, 'temperature': 0.7, 'context_length': 4096}
    llm = CTransformers(model="TheBloke/Mistral-7B-Instruct-v0.1-GGUF",
                        model_file="mistral-7b-instruct-v0.1.Q4_K_M.gguf",
                        config=config,
                        threads=os.cpu_count())
    map_template = """<s>[INST] The following is a part of a transcript:
    {docs}
    Based on this, please identify the main points.
    Answer:  [/INST] </s>"""
    map_prompt = PromptTemplate.from_template(map_template)
    map_chain = LLMChain(llm=llm, prompt=map_prompt)

    # Reduce template and chain
    reduce_template = """<s>[INST] The following is set of summaries from the transcript:
    {doc_summaries}
    Take these and distill it into a final, consolidated summary of the main points.
    Construct it as a well organized summary of the main points and should be between 3 and 5 paragraphs.
    Answer:  [/INST] </s>"""
    reduce_prompt = PromptTemplate.from_template(reduce_template)
    reduce_chain = LLMChain(llm=llm, prompt=reduce_prompt)
     combine_documents_chain = StuffDocumentsChain(
        llm_chain=reduce_chain, document_variable_name="doc_summaries"
    )
    # Combines and iteratively reduces the mapped documents
    reduce_documents_chain = ReduceDocumentsChain(
        # This is final chain that is called.
        combine_documents_chain=combine_documents_chain,
        # If documents exceed context for `StuffDocumentsChain`
        collapse_documents_chain=combine_documents_chain,
        # The maximum number of tokens to group documents into.
        token_max=4000,
    )
    # Combining documents by mapping a chain over them, then combining results
    map_reduce_chain = MapReduceDocumentsChain(
        # Map chain
        llm_chain=map_chain,
        # Reduce chain
        reduce_documents_chain=reduce_documents_chain,
        # The variable name in the llm_chain to put the documents in
        document_variable_name="docs",
        # Return the results of the map steps in the output
        return_intermediate_steps=True,
    )
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=4000, chunk_overlap=0
    )
    split_docs = text_splitter.split_documents(docs)

    # Run the chain
    start_time = time.time()
    result = map_reduce_chain.__call__(split_docs, return_only_outputs=True)
    print(f"Time taken: {time.time() - start_time} seconds")
    return result['output_text']