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stream-shorts/src/StreamShorts.Console/Program.cs
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using System.Globalization;
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using System.Resources;
using System.Text;
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using System.Text.Json;
using System.Text.Json.Serialization;
using FFMpegCore;
using FFMpegCore.Enums;
using FFMpegCore.Pipes;
using NAudio.Wave;
using Whisper.net;
using Whisper.net.Ggml;
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Log.Logger = new LoggerConfiguration()
.WriteTo.File(
formatter: new CompactJsonFormatter(),
path: Path.Combine(AppContext.BaseDirectory, "logs", "log.jsonl"),
rollingInterval: RollingInterval.Day
)
.Enrich.FromLogContext()
.MinimumLevel.Verbose()
.MinimumLevel.Override("Microsoft", LogEventLevel.Fatal)
.CreateLogger();
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try
{
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var appName = Assembly.GetExecutingAssembly().GetName().Name;
Log.Information("Starting {AppName}", appName);
await Host.CreateDefaultBuilder(args)
.ConfigureLogging(static l => l.ClearProviders())
.ConfigureServices(static (_, services) =>
{
services.AddSingleton(AnsiConsole.Console);
services.AddSingleton<IFileSystem, FileSystem>();
services.AddSingleton<IAudioExtractor, AudioExtractor>();
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})
.BuildApp()
.RunAsync(args);
Log.Information("{AppName} has completed successfully.", appName);
}
catch (Exception ex)
{
Log.Fatal(ex, "An unhandled exception occurred during execution.");
throw;
}
finally
{
await Log.CloseAndFlushAsync();
}
// using var mp3Stream = new MemoryStream();
// using var mp4Stream = new FileStream(args[0], FileMode.Open, FileAccess.Read);
// var wasExtracted = await FFMpegArguments
// .FromPipeInput(new StreamPipeSource(mp4Stream))
// .OutputToPipe(
// new StreamPipeSink(mp3Stream),
// o => o.DisableChannel(Channel.Video).ForceFormat("mp3")
// )
// .ProcessAsynchronously();
// // Step 2: Convert MP3 stream to 16khz wave format
// mp3Stream.Position = 0;
// using var reader = new Mp3FileReader(mp3Stream);
// var outFormat = new WaveFormat(16000, reader.WaveFormat.Channels);
// using var resampler = new MediaFoundationResampler(reader, outFormat);
// using var waveStream = new MemoryStream();
// WaveFileWriter.WriteWavFileToStream(waveStream, resampler);
// // Step 3: Split the wave stream into 2 minute segments
// waveStream.Position = 0;
// var segmentDuration = TimeSpan.FromMinutes(2);
// var segments = new List<MemoryStream>();
// using var waveReader = new WaveFileReader(waveStream);
// var segmentCount = (int)Math.Ceiling(waveReader.TotalTime.TotalMilliseconds / segmentDuration.TotalMilliseconds);
// Directory.CreateDirectory("segments");
// foreach (var i in Enumerable.Range(0, segmentCount))
// {
// waveStream.Position = 0;
// using var segmentWaveReader = new WaveFileReader(waveStream);
// var segment = segmentWaveReader.ToSampleProvider()
// .Skip(i * segmentDuration)
// .Take(segmentDuration);
// var segmentProvider = segment.ToWaveProvider16();
// var segmentStream = new MemoryStream();
// WaveFileWriter.WriteWavFileToStream(segmentStream, segmentProvider);
// segmentStream.Position = 0;
// segments.Add(segmentStream);
// }
// // Step 4: Transcribe each segment using Whisper
// using var modelMemoryStream = new MemoryStream();
// var model = await WhisperGgmlDownloader.Default.GetGgmlModelAsync(GgmlType.TinyEn);
// await model.CopyToAsync(modelMemoryStream);
// var whisperFactory = WhisperFactory.FromBuffer(modelMemoryStream.ToArray());
// using var whisperProcessor = whisperFactory.CreateBuilder()
// .WithLanguage("en")
// .Build();
// var completeTranscription = new StringBuilder();
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// foreach (var (i, segment) in segments.Select((s, index) => (index, s)))
// {
// var durationOffset = TimeSpan.FromMilliseconds(i * segmentDuration.TotalMilliseconds);
// var segmentTranscription = new StringBuilder();
// await foreach (var result in whisperProcessor.ProcessAsync(segment, CancellationToken.None))
// {
// var startTime = result.Start + durationOffset;
// var endTime = result.End + durationOffset;
// segmentTranscription.AppendLine(CultureInfo.CurrentCulture, $"[{startTime:hh\\:mm\\:ss} - {endTime:hh\\:mm\\:ss}] {result.Text}");
// }
// completeTranscription.Append(segmentTranscription);
// }
// // Step 5: Send the transcription to LLM for analysis
// // TODO: Explore this prompt further...seems break
// // when transcription is long
// var prompt = $$"""
// I need your help to transform my YouTube live stream transcript into engaging YouTube Shorts. Act as my content editor and pinpoint **all potential candidate segments** that are perfect for short-form video. I'm looking for clips that are:
//   * **Funny:** Moments that will make viewers laugh.
//   * **Informative:** Sections packed with valuable information or tips.
//   * **Insightful:** Portions offering unique perspectives or 'aha\!' moments.
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// For each suggested short, please provide:
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//   * The **start time** of the initial segment and the **end time** of the final segment. The duration of each short should be no longer than 3 minutes, but **aim for durations between 15 seconds and 60 seconds**. However, the short **must be as long as necessary to capture the complete thought or idea**, even if it means exceeding the target range or extending slightly to capture all necessary dialogue.
//   * A concise **title** that grabs attention.
//   * A brief **description** highlighting the short's content and its appeal.
//   * An **explanation** of why this particular segment is suitable for a YouTube Short, focusing on its potential for discoverability and engagement.
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// Please format your response as a JSON array of objects with the following structure:
// ```json
// {
//   ""title"": ""string"",
//   "start_time": "string",
//   "end_time": "string",
//   "description": "string",
//   "explanation": "string"
// }
// ```
// Here is the transcript of my YouTube live stream:
// {{completeTranscription}}
// """;
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// if (string.IsNullOrWhiteSpace(apiKey))
// {
// throw new InvalidOperationException("GeminiApiKey is not configured in appsettings.json.");
// }
// using var client = new HttpClient()
// {
// Timeout = TimeSpan.FromMinutes(30)
// };
// var requestUrl = $"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent?key={apiKey}";
// using var request = new HttpRequestMessage(HttpMethod.Post, requestUrl)
// {
// Content = new StringContent(
// JsonSerializer.Serialize(new
// {
// contents = new[]
// {
// new
// {
// role = "user",
// parts = new[]
// {
// new
// {
// text = prompt
// }
// },
// }
// },
// generationConfig = new
// {
// responseMimeType = "application/json",
// }
// }),
// Encoding.UTF8,
// "application/json"
// )
// };
// var response = await client.SendAsync(request);
// var responseContent = await response.Content.ReadAsStringAsync();
// var responseJson = JsonSerializer.Deserialize<LLMResponse>(responseContent);
// var candidatesText = responseJson?
// .Candidates?
// .FirstOrDefault()?
// .Content
// .Parts?.FirstOrDefault()?
// .Text;
// var analysis = JsonSerializer.Deserialize<List<LLMAnalysis>>(candidatesText ?? string.Empty);
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// if (analysis is null)
// {
// Console.WriteLine(resourceManager.GetString("LLMAnalysisFailed", CultureInfo.CurrentCulture));
// return;
// }
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// foreach (var result in analysis)
// {
// var fileName = string.Concat(result.Title.Split(Path.GetInvalidFileNameChars()));
// await FFMpeg.SubVideoAsync(
// args[0],
// $"{fileName}.mp4",
// result.StartTime,
// result.EndTime
// );
// }
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// // Step 6: Use analysis to generate a short video
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// record LLMAnalysis(
// [property: JsonPropertyName("title")]
// string Title,
// [property: JsonPropertyName("description")]
// string Description,
// [property: JsonPropertyName("explanation")]
// string Explanation,
// [property: JsonPropertyName("start_time")]
// TimeSpan StartTime,
// [property: JsonPropertyName("end_time")]
// TimeSpan EndTime
// );
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// record LLMResponse(
// [property: JsonPropertyName("candidates")]
// Candidate[] Candidates
// );
// record Candidate(
// [property: JsonPropertyName("content")]
// Content Content
// );
// record Content(
// [property: JsonPropertyName("parts")]
// Part[] Parts
// );
// record Part(
// [property: JsonPropertyName("text")]
// string Text
// );