Use logprobs, fix beam candidate selection

Incorrect sort condition resulted in worst 5 beams being picked instead of
best 5.

Use log probabilities for joint probability calculation instead of
linear probabilities. Long beams would have probabilities converge
exponentially towards zero; now they converge linearly towards
-INFINITY.

Using both transcripts in Evaluation/setup.ps1, I see a small edit
distance regression (~5%) using beam search vs. greedy.

4 files changed, 34 insertions, 41 deletions

diff --git a/Evaluate/.swp b/Evaluate/.swp
deleted file mode 100644
index c1bc460..0000000
--- a/Evaluate/.swp
+++ /dev/null
diff --git a/Evaluate/evaluate.py b/Evaluate/evaluate.py
index 5e8c85d..81b3edf 100644
--- a/Evaluate/evaluate.py
+++ b/Evaluate/evaluate.py
@@ -1,11 +1,12 @@
 import argparse
 import editdistance
-import jiwer
 import re
 import subprocess
 import sys
 import time
 
+from whisper.normalizers import EnglishTextNormalizer
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("reference_path", type=str, help="Path to reference transcript")
@@ -33,22 +34,15 @@ if __name__ == "__main__":
     with open(args.reference_path, "r") as f:
         ref_transcript = f.read()
 
+    # Normalize transcripts before computing edit distance (as described in
+    # whisper paper).
+    normalize = EnglishTextNormalizer()
+    test_transcript = normalize(test_transcript)
+    ref_transcript = normalize(ref_transcript)
+
     dist = editdistance.eval(ref_transcript, test_transcript)
-    wer_transform = jiwer.Compose([
-        jiwer.ToLowerCase(),
-        jiwer.RemoveWhiteSpace(replace_by_space=True),
-        jiwer.RemoveMultipleSpaces(),
-        jiwer.RemovePunctuation(),
-        jiwer.ReduceToListOfListOfWords(word_delimiter=" "),
-        ]) 
-    wer = jiwer.wer(
-            ref_transcript,
-            test_transcript,
-            truth_transform=wer_transform,
-            hypothesis_transform=wer_transform)
 
     print(f"Duration: {t1 - t0}")
     print(f"Levenshtein distance: {dist}")
-    print(f"Word error rate: {wer}")
     print(f"Transcript: {test_transcript}")
 
diff --git a/Whisper/Whisper/ContextImpl.cpp b/Whisper/Whisper/ContextImpl.cpp
index 22347cf..c52a167 100644
--- a/Whisper/Whisper/ContextImpl.cpp
+++ b/Whisper/Whisper/ContextImpl.cpp
@@ -68,13 +68,13 @@ HRESULT ContextImpl::decode( const int* tokens, size_t length, int n_past, int t
 
 std::pair<int, int> ContextImpl::beamGetMinJointProb() const
 {
-	float min_p = 1.1;
-	int min_p_beam;
-	int min_p_sample;
+	float min_p = INFINITY;
+	int min_p_beam = 0;
+	int min_p_sample = 0;
 	for (int nth_beam = 0; nth_beam < ctx_.size(); nth_beam++) {
 		for (int nth_best = 0; nth_best < ctx_[nth_beam].beam_ctx.best_tokens.size(); nth_best++) {
-			const float cur_p = ctx_[nth_beam].beam_ctx.joint_prob *
-				ctx_[nth_beam].beam_ctx.best_tokens[nth_best].p;
+			const float cur_p = ctx_[nth_beam].beam_ctx.joint_logprob +
+				log(ctx_[nth_beam].beam_ctx.best_tokens[nth_best].p);
 			if (cur_p < min_p) {
 				min_p = cur_p;
 				min_p_beam = nth_beam;
@@ -87,10 +87,10 @@ std::pair<int, int> ContextImpl::beamGetMinJointProb() const
 
 int ContextImpl::beamGetMaxJointProb() const
 {
-	float max_p = -0.1;
-	int max_p_beam;
+	float max_p = -INFINITY;
+	int max_p_beam = 0;
 	for (int nth_beam = 0; nth_beam < ctx_.size(); nth_beam++) {
-		const float cur_p = ctx_[nth_beam].beam_ctx.joint_prob;
+		const float cur_p = ctx_[nth_beam].beam_ctx.joint_logprob;
 		if (cur_p > max_p) {
 			max_p = cur_p;
 			max_p_beam = nth_beam;
@@ -643,7 +643,7 @@ HRESULT COMLIGHTCALL ContextImpl::runFullImpl( const sFullParams& params, const
 
 			ctx.loop_ctx.tokens_cur.clear();
 			ctx.loop_ctx.result_len = 0;
-			ctx.beam_ctx.joint_prob = 1.0;
+			ctx.beam_ctx.joint_logprob = 0.0;
 			ctx.beam_ctx.beam_done = false;
 			ctx.seek_delta = 100 * WHISPER_CHUNK_SIZE;
 			// have we already sampled a non-beg timestamp token for the current segment?
@@ -757,18 +757,12 @@ HRESULT COMLIGHTCALL ContextImpl::runFullImpl( const sFullParams& params, const
 						auto& prompt = ctx_[nth_beam].loop_ctx.prompt;
 						auto& tokens_cur = ctx_[nth_beam].loop_ctx.tokens_cur;
 
-						if (prompt.empty()) {
-							// The current beam is already complete.
-							// TODO we can probably elide this assignment.
-							probs = probs_prev;
-						}
-						else {
+						if (!prompt.empty()) {
 							CHECK(decode(prompt.data(), prompt.size(), n_past,
 								params.cpuThreads, nth_beam));
 							n_past += (int)prompt.size();
 							prompt.clear();
 							probs_prev = probs;
-						}
 
 						// For each beam, pick the top `beam_wd` most likely
 						// tokens.
@@ -777,6 +771,7 @@ HRESULT COMLIGHTCALL ContextImpl::runFullImpl( const sFullParams& params, const
 							? sampleTimestampN(true, nth_beam, beam_wd)
 							: sampleBestN(nth_beam, beam_wd);
 						ctx_[nth_beam].beam_ctx.best_tokens = tokens;
+						}
 					}
 
 					// Of the (`beam_wd` * `n_beams`) selected tokens, identify
@@ -791,7 +786,7 @@ HRESULT COMLIGHTCALL ContextImpl::runFullImpl( const sFullParams& params, const
 							best_beams_and_tokens[nth_beam] = { nth_beam, 0 };
 						}
 						for (int nth_beam = 0; nth_beam < ctx_.size(); nth_beam++) {
-							const auto& joint_prob = ctx_[nth_beam].beam_ctx.joint_prob;
+							const auto& joint_prob = ctx_[nth_beam].beam_ctx.joint_logprob;
 							for (int nth_best = 0; nth_best < beam_wd; nth_best++) {
 								// We want to see if this (beam, sample) combo is better than
 								// something in the current list of possibilities. To do that,
@@ -799,11 +794,11 @@ HRESULT COMLIGHTCALL ContextImpl::runFullImpl( const sFullParams& params, const
 								// against it.
 								const auto& min_p_beam = min_beam_sample.first;
 								const auto& min_p_sample = min_beam_sample.second;
-								const float min_p = ctx_[min_p_beam].beam_ctx.joint_prob *
-									ctx_[min_p_beam].beam_ctx.best_tokens[min_p_sample].p;
+								const float min_p = ctx_[min_p_beam].beam_ctx.joint_logprob +
+									log(ctx_[min_p_beam].beam_ctx.best_tokens[min_p_sample].p);
 
 								const auto& token = ctx_[nth_beam].beam_ctx.best_tokens[nth_best];
-								const float cur_p = ctx_[nth_beam].beam_ctx.joint_prob * token.p;
+								const float cur_p = ctx_[nth_beam].beam_ctx.joint_logprob + log(token.p);
 								if (cur_p > min_p) {
 									// Better parse found. Record.
 									// TODO this could be done in constant time using a heap.
@@ -819,11 +814,11 @@ HRESULT COMLIGHTCALL ContextImpl::runFullImpl( const sFullParams& params, const
 						}
 					}
 
-					// Sort parse in ascending order of beam.
+					// Sort parse in descending order of beam.
 					std::sort(best_beams_and_tokens.begin(),
 						best_beams_and_tokens.end(),
 						[](const std::pair<int, int> a, const std::pair<int, int> b) {
-							return a.first < b.first;
+							return a.first > b.first;
 						});
 
 					// Extract tokens for new parse.
@@ -848,7 +843,7 @@ HRESULT COMLIGHTCALL ContextImpl::runFullImpl( const sFullParams& params, const
 					bool ts_went_backwards = false;
 					for (int nth_beam = 0; nth_beam < ctx_.size(); nth_beam++) {
 						auto& beam_done = ctx_[nth_beam].beam_ctx.beam_done;
-						auto& joint_prob = ctx_[nth_beam].beam_ctx.joint_prob;
+						auto& joint_logprob = ctx_[nth_beam].beam_ctx.joint_logprob;
 						auto& has_ts = ctx_[nth_beam].has_ts;
 						auto& prompt = ctx_[nth_beam].loop_ctx.prompt;
 						auto& result_len = ctx_[nth_beam].loop_ctx.result_len;
@@ -856,6 +851,10 @@ HRESULT COMLIGHTCALL ContextImpl::runFullImpl( const sFullParams& params, const
 						auto& tokens_cur = ctx_[nth_beam].loop_ctx.tokens_cur;
 						auto& token = new_tokens[nth_beam];
 
+						if (!tokens_cur.empty() && tokens_cur[tokens_cur.size() - 1].id != model.vocab.token_eot) {
+							beam_done = false;
+						}
+
 						if (!beam_done) {
 							// Timestamp token: update sliding window.
 							if (token.id > model.vocab.token_beg) {
@@ -871,7 +870,7 @@ HRESULT COMLIGHTCALL ContextImpl::runFullImpl( const sFullParams& params, const
 
 							prompt.push_back(token.id);
 							tokens_cur.push_back(token);
-							joint_prob *= token.p;
+							joint_logprob += log(token.p);
 						}
 						if (token.id == model.vocab.token_eot ||
 							(params.max_tokens > 0 && i >= params.max_tokens) ||
diff --git a/Whisper/Whisper/ContextImpl.h b/Whisper/Whisper/ContextImpl.h
index 2f34126..7357af3 100644
--- a/Whisper/Whisper/ContextImpl.h
+++ b/Whisper/Whisper/ContextImpl.h
@@ -59,9 +59,9 @@ namespace Whisper
 				// Some beams may finish earlier than others, in which case
 				// we'd like to avoid re-running inference.
 				std::vector<float> probs_prev;
-				// Joint probability of every token leading up to the current
-				// context.
-				float joint_prob;
+				// Joint log-probability of every token leading up to the
+				// current context.
+				float joint_logprob;
 				bool beam_done;
 			};
 			AudioFrameContext loop_ctx;