Added workign Audioshake API implementation for alternate transcripti…

…on option

lyrics_transcriber/audioshake_transcriber.py

@@ -1,35 +1,93 @@
-import logging
 import requests
+import time
+import os
+import json
 
 
 class AudioShakeTranscriber:
-    def __init__(self, api_token, log_level=logging.DEBUG):
+    def __init__(self, api_token, logger):
         self.api_token = api_token
-        self.logger = logging.getLogger(__name__)
-        self.logger.setLevel(log_level)
+        self.base_url = "https://groovy.audioshake.ai"
+        self.logger = logger
 
     def transcribe(self, audio_filepath):
-        # This is a placeholder for the actual AudioShake API implementation
         self.logger.info(f"Transcribing {audio_filepath} using AudioShake API")
 
-        self.logger.debug(f"AudioShake API token: {self.api_token}")
-        # TODO: Implement the actual API call to AudioShake
-        # For now, we'll return a dummy result
-        return {
-            "transcription_data_dict": {
-                "segments": [
-                    {
-                        "start": 0,
-                        "end": 5,
-                        "text": "This is a dummy transcription",
-                        "words": [
-                            {"text": "This", "start": 0, "end": 1},
-                            {"text": "is", "start": 1, "end": 2},
-                            {"text": "a", "start": 2, "end": 3},
-                            {"text": "dummy", "start": 3, "end": 4},
-                            {"text": "transcription", "start": 4, "end": 5},
-                        ],
-                    }
-                ]
-            }
+        # Step 1: Upload the audio file
+        asset_id = self._upload_file(audio_filepath)
+        self.logger.debug(f"File uploaded successfully. Asset ID: {asset_id}")
+
+        # Step 2: Create a job for transcription and alignment
+        job_id = self._create_job(asset_id)
+        self.logger.debug(f"Job created successfully. Job ID: {job_id}")
+
+        # Step 3: Wait for the job to complete and get the results
+        result = self._get_job_result(job_id)
+        self.logger.debug(f"Job completed. Processing results...")
+
+        # Step 4: Process the result and return in the required format
+        return self._process_result(result)
+
+    def _upload_file(self, filepath):
+        self.logger.debug(f"Uploading {filepath} to AudioShake")
+        url = f"{self.base_url}/upload"
+        headers = {"Authorization": f"Bearer {self.api_token}"}
+        with open(filepath, "rb") as file:
+            files = {"file": (os.path.basename(filepath), file)}
+            response = requests.post(url, headers=headers, files=files)
+
+        self.logger.debug(f"Upload response status code: {response.status_code}")
+        self.logger.debug(f"Upload response content: {response.text}")
+
+        response.raise_for_status()
+        return response.json()["id"]
+
+    def _create_job(self, asset_id):
+        self.logger.debug(f"Creating job for asset {asset_id}")
+        url = f"{self.base_url}/job/"
+        headers = {"Authorization": f"Bearer {self.api_token}", "Content-Type": "application/json"}
+        data = {
+            "metadata": {"format": "json", "name": "alignment", "language": "en"},
+            "callbackUrl": "https://example.com/webhook/alignment",
+            "assetId": asset_id,
         }
+        response = requests.post(url, headers=headers, json=data)
+        response.raise_for_status()
+        return response.json()["job"]["id"]
+
+    def _get_job_result(self, job_id):
+        self.logger.debug(f"Getting job result for job {job_id}")
+        url = f"{self.base_url}/job/{job_id}"
+        headers = {"Authorization": f"Bearer {self.api_token}", "Content-Type": "application/json"}
+        while True:
+            response = requests.get(url, headers=headers)
+            response.raise_for_status()
+            job_data = response.json()["job"]
+            if job_data["status"] == "completed":
+                return job_data
+            elif job_data["status"] == "failed":
+                raise Exception("Job failed")
+            time.sleep(5)  # Wait 5 seconds before checking again
+
+    def _process_result(self, job_data):
+        self.logger.debug(f"Processing result for job {job_data}")
+        output_asset = next((asset for asset in job_data["outputAssets"] if asset["name"] == "transcription.json"), None)
+
+        if not output_asset:
+            raise Exception("Transcription output not found in job results")
+
+        transcription_url = output_asset["link"]
+        response = requests.get(transcription_url)
+        response.raise_for_status()
+        transcription_data = response.json()
+
+        transcription_data = {"segments": transcription_data.get("lines", []), "text": transcription_data.get("text", "")}
+
+        # Ensure each segment has the required fields
+        for segment in transcription_data["segments"]:
+            if "words" not in segment:
+                segment["words"] = []
+            if "text" not in segment:
+                segment["text"] = " ".join(word["text"] for word in segment["words"])
+
+        return transcription_data

lyrics_transcriber/transcriber.py

@@ -944,39 +944,39 @@ def transcribe(self):
         transcription_cache_suffix = "-audioshake" if self.audioshake_api_token else "-whisper"
         self.outputs["transcription_data_filepath"] = self.get_cache_filepath(f"{transcription_cache_suffix}.json")
 
-        whisper_cache_filepath = self.outputs["transcription_data_filepath"]
-        if os.path.isfile(whisper_cache_filepath):
-            self.logger.debug(f"transcribe found existing file at whisper_cache_filepath, reading: {whisper_cache_filepath}")
-            with open(whisper_cache_filepath, "r") as cache_file:
+        transcription_cache_filepath = self.outputs["transcription_data_filepath"]
+        if os.path.isfile(transcription_cache_filepath):
+            self.logger.debug(f"transcribe found existing file at transcription_cache_filepath, reading: {transcription_cache_filepath}")
+            with open(transcription_cache_filepath, "r") as cache_file:
                 self.outputs["transcription_data_dict"] = json.load(cache_file)
                 return
 
         if self.audioshake_api_token:
             self.logger.debug(f"Using AudioShake API for transcription")
             from .audioshake_transcriber import AudioShakeTranscriber
 
-            audioshake = AudioShakeTranscriber(self.audioshake_api_token, log_level=self.log_level)
-            result = audioshake.transcribe(self.audio_filepath)
+            audioshake = AudioShakeTranscriber(self.audioshake_api_token, logger=self.logger)
+            transcription_data = audioshake.transcribe(self.audio_filepath)
         else:
             self.logger.debug(f"Using Whisper for transcription with model: {self.transcription_model}")
             audio = whisper.load_audio(self.audio_filepath)
             model = whisper.load_model(self.transcription_model, device="cpu")
-            result = whisper.transcribe(model, audio, language="en", vad="auditok", beam_size=5, temperature=0.2, best_of=5)
+            transcription_data = whisper.transcribe(model, audio, language="en", vad="auditok", beam_size=5, temperature=0.2, best_of=5)
 
             # Remove segments with no words, only music
-            result["segments"] = [segment for segment in result["segments"] if segment["text"].strip() != "Music"]
-            self.logger.debug(f"Removed 'Music' segments. Remaining segments: {len(result['segments'])}")
+            transcription_data["segments"] = [segment for segment in transcription_data["segments"] if segment["text"].strip() != "Music"]
+            self.logger.debug(f"Removed 'Music' segments. Remaining segments: {len(transcription_data['segments'])}")
 
             # Split long segments
             self.logger.debug("Starting to split long segments")
-            result["segments"] = self.split_long_segments(result["segments"], max_length=36)
-            self.logger.debug(f"Finished splitting segments. Total segments after splitting: {len(result['segments'])}")
+            transcription_data["segments"] = self.split_long_segments(transcription_data["segments"], max_length=36)
+            self.logger.debug(f"Finished splitting segments. Total segments after splitting: {len(transcription_data['segments'])}")
 
-        self.logger.debug(f"writing transcription data JSON to cache file: {whisper_cache_filepath}")
-        with open(whisper_cache_filepath, "w") as cache_file:
-            json.dump(result, cache_file, indent=4)
+        self.logger.debug(f"writing transcription data JSON to cache file: {transcription_cache_filepath}")
+        with open(transcription_cache_filepath, "w") as cache_file:
+            json.dump(transcription_data, cache_file, indent=4)
 
-        self.outputs["transcription_data_dict"] = result
+        self.outputs["transcription_data_dict"] = transcription_data
 
     def get_cache_filepath(self, extension):
         filename = os.path.split(self.audio_filepath)[1]